Bidirectional Mendelian Randomization identifies plasma proteins associated with Cervical Cancer risk

Bidirectional Mendelian Randomization identifies plasma proteins associated with Cervical Cancer risk | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Bidirectional Mendelian Randomization identifies plasma proteins associated with Cervical Cancer risk Yan-Hong Zhao, Qing-Fen Ruan, Jing-Hua Ning, Xin Zhang, Run Qu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6551278/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Cervical cancer continues to pose a considerable challenge to global health, necessitating innovative approaches for improved diagnostics and personalized treatment strategies. Prior investigations have suggested that plasma proteins may play a role in the pathogenesis of cervical cancer; however, these studies do not confirm a causal relationship. To address this gap, conducted a large-scale Mendelian randomization (MR) study of the plasma proteome. Methods We conducted a two-sample bidirectional Mendelian randomization (MR) analysis of 4,907 plasma proteins using publicly available genome-wide association study (GWAS) summary statistics to investigate the causal relationship between plasma proteome and cervical cancer risk. Analytical methods included inverse variance weighting (IVW), weighted median, MR-Egger regression, and simple and weighted models. Additionally, we performed sensitivity analyses to evaluate heterogeneity and horizontal pleiotropy through Cochran's Q test, MR-Egger intercept, MR-PRESSO test, and leave-one-out analysis. We also applied false discovery rate (FDR) correction to the results of all inverse variance weighting (IVW) methods to identify the plasma proteins most strongly associated with cervical cancer. Finally, we enriched the most relevant plasma protein genes using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses and GeneMANIA to identify disease-related pathways. Results According to the IVW method, seven plasma proteins are significantly associated with cervical cancer risk (FDR-adjusted p < 0.05). Specifically, six proteins demonstrated protective factors: DEFB135 (OR = 0.201, 95% CI = 0.082–0.492, p < 0.001), FGL2 (OR = 0.104, 95% CI = 0.032–0.338, p < 0.001), FTMT (OR = 0.612, 95% CI = 0.465–0.804, p < 0.001), PDIA4 (OR = 0.088, 95% CI = 0.026–0.295, p < 0.001), SPHK2 (OR = 0.102, 95% CI = 0.030–0.350, p < 0.001), and TMED2 (OR = 0.045, 95% CI = 0.008–0.246, p < 0.001). In contrast, RACGAP1 (OR = 1.755, 95% CI = 1.286–2.395, p 0.05) between cervical cancer and these plasma proteins. Functional enrichment analysis identified several biologically relevant pathways potentially involved in cervical cancer pathogenesis, including the establishment of organelle localization, regulation of oxidoreductase activity, Ferroptosis, and Porphyrin metabolism. Conclusion These findings suggest that DEFB135, FGL2, FTMT, PDIA4, SPHK2, and TMED2 may protect against cervical cancer, while RACGAP1 may represent a potential risk factor. The identified tumor markers provide mechanistic insights into the molecular basis of cervical cancer and warrant further investigation in functional studies. Plasma proteins cervical cancer Mendelian randomization and tumor markers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Cervical cancer is the leading cause of cancer-related deaths among women worldwide. Recent epidemiological data from the International Agency for Research on Cancer (IARC) estimates its incidence and mortality rates are particularly significant among common female cancers[ 1 ]. The primary risk factor for cervical cancer is persistent infection with high-risk subtypes of human papillomavirus (HPV). The E6 and E7 proteins of HPV promote the malignant transformation of cells and tumorigenesis by interacting with host cell proteins[ 2 ]. Current therapeutic strategies for cervical cancer rely heavily on platinum-based chemotherapeutic agents, such as cisplatin; however, the emergence of drug resistance frequently compromises treatment efficacy and contributes to disease recurrence[ 3 ]. The World Health Organization (WHO) emphasizes that early detection and timely intervention are critical for reducing the burden of cervical cancer[ 4 ]. Therefore, Screening for potential targets to combat cervical cancer is essential. In recent years, plasma proteins have emerged as significant targets for drug development due to their ability to diagnose and predict diseases, identify therapeutic targets, and elucidate the pathophysiology of various conditions[ 5 ]. Previous studies have demonstrated the high independent diagnostic value of squamous cell carcinoma antigen (SCCA) as a tumor biomarker for cervical cancer[ 6 ]. CA125 has been recognized as a biomarker for the diagnosis and prognosis of cervical cancer[ 7 ]. Furthermore, A study based on the UK Biobank cohort found that the PAX8, CLPTM1L, and HLA genes play a role in cervical carcinogenesis[ 8 ]. However, traditional observational studies are often limited by confounding factors and reverse causality, leading to inconclusive findings. To address these limitations. Recent research has integrated protein quantitative trait loci (pQTLs) into Mendelian randomization (MR) analyses, enabling the prioritization of drug targets with enhanced causal inference[ 9 ]. Mendelian randomization utilizes single nucleotide polymorphisms (SNPs) as instrumental variables to infer causal relationships between exposure factors and outcomes[ 10 ], minimizing bias from confounding factors and avoiding interference from reverse causation[ 11 ]. With the rapid advancement of high-throughput proteomics techniques in plasma analysis, researchers have established a causal relationship between plasma proteins and various diseases, including multiple sclerosis, acne, and breast cancer, through Mendelian Randomization (MR) analysis[ 12 – 14 ]. However, the causal relationship between plasma proteins and cervical cancer remains poorly understood. Therefore, we leveraged the largest available plasma proteomics dataset to perform a comprehensive two-sample bidirectional MR analysis, examining the causal effects of 4,907 plasma proteins on cervical cancer risk. Our findings provide novel insights into the molecular mechanisms underlying cervical cancer and establish a theoretical base for advancing early Screening, diagnostic strategies, and therapeutic interventions for this disease. Materials and methods Study Design The study design is presented in Fig. 2 . Large-scale proteomics data were analyzed using R software (v 4.3.2). We employed a two-sample bidirectional Mendelian randomization (MR) method to investigate the causal relationship between 4,907 plasma proteins and cervical cancer. Furthermore, we conducted extensive sensitivity analyses to ensure our findings were robust and reliable. Finally, functional enrichment and GeneMANIA analyses were performed on the plasma protein-coding genes most significantly associated with cervical cancer. MR analysis relies on three fundamental assumptions: (a) the relevance assumption, instrumental variables (IVs) must be closely related to the exposure factors; (b) the independence assumption, instrumental variables should be independent of any confounders related to both the exposure and the outcome; and (c) the exclusion restriction assumption: instrumental variables should only influence the outcome through the exposure factors, rather than acting directly on the outcome (Fig. 1 ). Data Sources The exposure data utilized in this study were obtained from a publicly accessible proteomics genome-wide association study (GWAS) dataset available through the deCODE database ( https://www.decode.com ). This dataset is derived from a comprehensive protein quantitative trait loci (pQTL) analysis conducted on a cohort of 35,559 Icelandic individuals, from which 4,907 plasma proteins were identified (Supplemental Table 1)[ 15 ]. Genome-wide association study (GWAS) data for cervical cancer (GWAS ID: finngen_R10_C3_CERVIX_UTERI_EXALLC) were obtained from the Finnish database ( https://www.finngen.fi/en/access_results ). Specifically, we utilized cohort data labeled as "Malignant neoplasm of the cervix (controls excluding all cancers)," which included 388 cases and 182,927 controls (R10) (Table 1 ) (Supplemental Table 2). To minimize bias due to population heterogeneity, we restricted the analysis to individuals of European descent[ 16 ]. Additionally, the relevant ethical committees approved all data used in this study, and no further ethical approvals were necessary. Table 1 The data sources utilized for the Mendelian randomization analysis within the study. Database Sample size Population Plasma protein deCODE 4907 Icelander CC FinnGen 388 European Selection of Instrumental Variables To identify valid instrumental variables (IVs), we implemented rigorous selection criteria using the TwoSampleMR package in R (v 4.3.2). First, to satisfy the relevance assumption of Mendelian randomization (MR) analysis, we selected single nucleotide polymorphisms (SNPs) that were closely associated with the exposure, applying a threshold of p < 5×10^ −8 [ 17 ]. Second, to address potential linkage disequilibrium (LD), we performed SNP clumping with 10,000 kb and r2 < 0.001. Third, to minimize bias from weak instruments, we calculated the F-statistic for each SNP using the formula (F = R^2*(n-2)/(1-R^2); R^2 = 2*(1-MAF) *MAF*beta^2/(2*(1-MAF) *MAF*beta^2 + 2*(1-MAF) *MAF*se^2*n)). Only IVs with an F-statistic > 10 were retained for downstream analyses. Finally, we excluded SNPs with palindromic structures when harmonizing exposure and outcome data using the harmonise_data function[ 18 ]. For reverse MR analysis, we applied a significance threshold of p < 5×10^ −6 to identify SNPs strongly associated with cervical cancer (CC). In contrast, for forward MR analysis, a threshold of p < 5×10^ −8 was sufficient to ensure adequate plasma protein IVs. Forward Mendelian Randomization Analysis To assess the causal relationship between 4,907 plasma proteins and cervical cancer, we applied five distinct Mendelian randomization (MR) methods. The inverse variance weighted (IVW) method integrates the Wald ratio estimates of the causal effects of various single nucleotide polymorphisms (SNPs) to assess the causal relationship between exposure and outcome, making it the predominant analytic method[ 19 ]. The weighted median approach is more robust against invalid instrumental variables and provides reliable estimates when valid instrumental variables account for more than 50% of the weight[ 20 ]. The MR-Egger regression method evaluates invalid causal hypotheses and ensures consistency in assessing causality, particularly in cases where instrumental variables exhibit insufficient genetic variation[ 21 ]. The weighted mode effectively captures the most representative causal relationships by emphasizing the effect values of the plurality, especially when multiple instrumental variables are involved. Additionally, the simple mode is another analytical method based on the effects of plurality, which is more resilient to extreme values and less influenced by small amounts of outlier data. When different analytical methods yield inconsistent results, the IVW method should be prioritized. Sensitivity Analysis Sensitivity analyses were conducted to ensure the robustness and reliability of our findings. These included Cochran's Q test, MR-Egger intercept, MR-PRESSO, and leave-one-out analysis. The heterogeneity of the findings was analyzed using Cochran's Q test; a p > 0.05 indicates the absence of heterogeneity[ 22 ]. The pleiotropy of relationships between instrumental variables and other potential confounders was assessed through the MR-Egger method. A result of p < 0.05 in the MR-Egger intercept analysis suggests the presence of horizontal pleiotropy. Additionally, the MR-PRESSO method was explicitly employed to identify outlier SNPs that exhibited horizontal pleiotropy, provided that more than 50% of the instrumental variables were valid[ 23 ]. These comprehensive sensitivity analyses collectively strengthened the validity of our causal inferences. Furthermore, we conducted a leave-one-out analysis, which reassessed the effect values of the remaining SNPs by sequentially removing each SNP. This approach aimed to validate the results' reliability and robustness by evaluating each SNP's impact on the findings. Scatter plots were generated to visually represent the causal relationships between genetic instruments and outcomes. Additionally, forest plots were employed to display effect sizes and their corresponding statistical significance. Reverse Mendelian Randomization Analysis To further elucidate the causal relationship between exposure and outcome, we conducted a reverse Mendelian randomization (MR) analysis along with a sensitivity analysis, using cervical cancer (CC) as the exposure factor and plasma proteins associated with CC, identified in the forward MR analysis, as the outcome. This approach facilitated the identification of potential reverse causality, mitigated the effects of confounding factors, and ensured the robustness of our study. The analytical and statistical methods employed were as previously described[ 24 ]. GO/KEGG Pathway Enrichment and GeneMANIA Analysis To elucidate the biological relevance of the identified plasma proteins in cervical cancer (CC), we performed functional annotation and pathway enrichment analyses using the clusterProfiler package (V 4.10.1) in R (v 4.3.2). Gene Ontology (GO) analysis was conducted to categorize the biomarker genes into three functional domains: biological processes (BP), molecular functions (MF), and cellular components (CC). Concurrently, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was employed to identify metabolic and signaling pathways associated with these genes. A significance threshold of p < 0.05 was applied for both GO and KEGG analyses. Furthermore, to explore potential protein-protein interactions, we constructed a protein-protein interaction (PPI) network using GeneMANIA ( http://genemania.org/ ), providing insights into the functional relationships among the identified biomarkers. Results Forward MR analysis First, we selected exposure single nucleotide polymorphisms (SNPs) at a genome-wide significance threshold of p < 5×10^ −8 , identifying 14,475 SNPs linked to plasma proteins as instrumental variables (IVs). We then performed a two-sample Mendelian randomization (MR) analysis for each of the 4,907 plasma proteins using five distinct analytical methods. The results were further refined using the inverse variance weighted (IVW) method, applying a significance threshold of p < 0.05 and a false discovery rate (FDR) < 0.2. Ultimately, we identified seven plasma protein phenotypes significantly associated with CC: DEFB135, FGL2, FTMT, PDIA4, SPHK2, TMED2, and RACGAP1. The results of the Inverse Variance Weighting (IVW) analysis indicated that DEFB135 (OR = 0.201, 95% CI = 0.082–0.492, p < 0.001), FGL2 (OR = 0.104, 95% CI = 0.032–0.338, p < 0.001), FTMT (OR = 0.612, 95% CI = 0.465–0.804, p < 0.001), PDIA4 (OR = 0.088, 95% CI = 0.026–0.295, p < 0.001), SPHK2 (OR = 0.102, 95% CI = 0.030–0.350, p < 0.001), and TMED2 (OR = 0.045, 95% CI = 0.008–0.246, p < 0.001) were negatively correlated with colorectal cancer (CC), suggesting a potential protective effect. Conversely, RACGAP1 (OR = 1.755, 95% CI = 1.286–2.395, p < 0.001) exhibited a positive correlation with CC, indicating its possible role in disease pathogenesis (Fig. 4 ). These findings were further supported by complementary analyses, including MR-Egger regression, weighted median (WM), and both simple and weighted mode methods. To enhance the robustness of our results, we generated volcano plots to visualize the strength and significance of the associations (Fig. 3 ). Sensitivity analysis The robustness of our findings was validated through sensitivity analyses, including heterogeneity assessment using Cochran's Q test and evaluation of horizontal pleiotropy via the MR-Egger intercept test. The Cochran's Q test revealed that the p-values for the seven plasma proteins associated with cervical cancer (CC) were greater than 0.05, indicating no significant heterogeneity. Similarly, the p-values from the Egger-intercept test were also above 0.05, suggesting that they were not influenced by horizontal pleiotropy (Table 2 ). Further validation using the MR-PRESSO method confirmed the reliability of the Mendelian randomization (MR) results. Additionally, the leave-one-out method analysis demonstrated that our findings were not dependent on any single SNP (Fig. 5 ). Reverse Mendelian Randomization Analysis To assess potential reverse causality between the seven identified plasma proteins and cervical cancer (CC), we performed an inverse Mendelian randomization (MR) analysis. This analysis included the inverse variance weighted (IVW) method, MR-Egger regression, weighted median, simple mode, and weighted mode. The IVW results indicated no significant causal effects of the plasma proteins on CC risk: TMED2 (OR = 0.956, 95% CI = 0.909–1.005, p = 0.075), FGL2 (OR = 0.954, 95% CI = 0.885–1.029, p = 0.223), SPHK2 (OR = 0.955, 95% CI = 0.896–1.018, p = 0.156), DEFB135 (OR = 0.950, 95% CI = 0.898–1.006, p = 0.080), PDIA4 (OR = 0.948, 95% CI = 0.887–1.014, p = 0.121), FTMT (OR = 0.873, 95% CI = 0.724–1.054, p = 0.157), and RACGAP1 (OR = 1.083, 95% CI = 0.957–1.225, p = 0.205). Similarly, none of the supplementary MR methods yielded statistically significant causal estimates (all p > 0.05) (Fig. 6 ), providing no evidence to support reverse causality between these plasma proteins and CC. Sensitivity analysis of the reverse MR revealed Cochran's Q test p -values < 0.05, likely attributable to genetic variation influencing phenotypes and population-specific genetic backgrounds, potentially mediated through multiple pathways. However, the remaining plasma protein phenotypes showed no evidence of heterogeneity or horizontal pleiotropy, further supporting the robustness of our findings. GO/KEGG Pathway Enrichment and GeneMANIA Analysis In the GO analysis, the disease-associated plasma proteins were significantly enriched in specific biological processes (BP), cellular components (CC), and molecular functions (MF). For BP, the proteins were primarily involved in organelle localization and the regulation of oxidoreductase activity. For CC, enrichment was observed in pathways such as the zymogen granule membrane, COPI-coated vesicle membrane, zymogen granule, COPI-coated vesicle, Flemming body, spindle midzone, cleavage furrow, Golgi-associated vesicle membrane, ER to Golgi transport vesicle membrane, and cell division site. For MF, the proteins were enriched in functions including ferric iron binding, oxidoreductase activity, acting on metal ions, bioactive lipid receptor activity, protein disulfide isomerase activity, intramolecular oxidoreductase activity, transposing S-S bonds, ferrous iron binding, protein-disulfide reductase activity, gamma-tubulin binding, frizzled binding, and phosphatidylinositol-3,4,5-trisphosphate binding. The number of plasma proteins enriched in each pathway exceeded one, as indicated on the abscissa, and all enrichments were statistically significant ( p < 0.05). The highest confidence level was observed for biological processes ( p < 0.01). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that the disease-associated plasma proteins were predominantly involved in ferroptosis, porphyrin metabolism, Vibro cholerae infection, sphingolipid metabolism, VEGF signaling pathway, thyroid hormone synthesis, Fc gamma R-mediated phagocytosis, Sphingolipid signaling pathway, and Apelin signaling pathway. Among these pathways, the highest statistical confidence ( p < 0.01) was observed for ferroptosis and porphyrin metabolism (Fig. 7 ). Furthermore, GeneMANIA network analysis demonstrated extensive functional interactions among the plasma protein phenotype genes, with co-expression accounting for 92.52% and predicted interactions representing 7.48% of the gene interaction network (Fig. 8 ). These findings highlight the complex functional relationships and potential biological mechanisms underlying the roles of these plasma proteins in cervical cancer. Table 2 Sensitivity analysis of the forward Mendelian randomization (MR) study with cervical cancer (CC) as the outcome. Heterogeneity analysis Pleiotropy analysis Inverse variance weighted MR Egger MR Egger Q Q_df Q_pval Q Q_df Q_pval Egger_intercept se pval DEFB135 2.7523 4 0.6000 1.8838 3 0.5969 0.1151 0.1151 0.4201 FGL2 3.5316 3 0.3167 2.9013 2 0.2344 0.1726 0.2618 0.5776 FTMT 5.5965 10 0.8479 5.5133 9 0.7875 -0.0166 0.0574 0.7795 PDIA4 2.9094 3 0.4058 2.5884 2 0.2741 0.1525 0.3062 0.6679 RACGAP1 9.5805 11 0.5685 9.0124 10 0.5309 0.0463 0.0614 0.4684 SPHK2 2.0807 3 0.5558 1.4094 2 0.4942 0.1922 0.2346 0.4987 TMED2 2.4461 2 0.2943 2.4457 1 0.1178 -0.0052 0.4426 0.9926 Discussion Leveraging large-scale protein quantitative trait locus (pQTL) data and publicly available genome-wide association study (GWAS) summary statistics, we employed Mendelian randomization (MR) analysis to investigate the causal relationship between 4,907 plasma proteins and cervical cancer. Our analysis identified seven plasma proteins with significant causal associations: DEFB135, FGL2, FTMT, PDIA4, SPHK2, and TMED2, which may have protective effects against cervical cancer, whereas RACGAP1 may promote cervical carcinogenesis. We conducted sensitivity analyses to minimize the impact of heterogeneity and horizontal pleiotropy on our findings. Furthermore, reverse MR analysis showed no evidence that cervical carcinogenesis drives these plasma proteins' aberrant expression, reinforcing the robustness of the causal relationships. Functional enrichment analyses using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed that these plasma proteins primarily involve oxidoreductase activity and ferroptosis regulation pathways. Ferroptosis is characterized by iron-dependent lipid peroxidation, which disrupts cellular membranes and culminates in cell death. This process has been implicated in the pathogenesis of various diseases, including cancer. GeneMANIA analysis indicated that the seven plasma proteins identified as phenotypic genes were situated at the center of the network, forming a dense web of interactions with surrounding genes. These findings provide mechanistic insights into the molecular underpinnings of cervical cancer and highlight potential therapeutic targets, offering a foundation for further experimental and clinical investigations. The DEFB135 gene encodes defensin beta 135, an antimicrobial peptide critical in modulating immune responses and inflammation, predominantly expressed in human epithelial tissues[ 25 ]. Previous studies have shown that DEFB135 expression levels are associated with susceptibility to certain infectious diseases. Cervical cancer is primarily driven by persistent infection with high-risk human papillomavirus (HPV); it is possible that HPV-mediated downregulation of DEFB135 expression could impair the host's immune defense mechanisms. Although no direct link between DEFB135 and oncogenesis has been established, our Mendelian randomization (MR) analysis revealed a significant inverse association between DEFB135 and cervical cancer risk. This finding suggests that DEFB135 may be protective in mitigating disease progression and could represent a potential therapeutic target. However, the precise molecular mechanisms underlying this relationship remain to be elucidated and warrant further investigation. Fibrinogen-like protein 2 (FGL2), a member of the fibrinogen superfamily, is characterized by its thromboplastin activity and diverse immunomodulatory functions. FGL2 is frequently overexpressed in various tumor tissues [ 26 – 28 ] and has been implicated in the malignant progression of low-grade gliomas (LGGs) to high-grade gliomas (HGGs)[ 29 ]. Studies have also demonstrated that FGL2 promotes macrophage polarization and enhances the proliferation of regulatory T cells (Tregs) within the tumor microenvironment, thereby amplifying immunosuppressive activity[ 30 ]. In hepatocellular carcinoma, FGL2 overexpression has been closely linked to tumor growth and angiogenesis, suggesting a potential parallel role in cervical cancer[ 31 ]. However, the functional significance of FGL2 in cervical cancer remains poorly understood. Our findings indicate that elevated FGL2 expression may have a protective effect against cervical cancer, highlighting the need for further ex vivo and in vivo studies to elucidate the mechanism of this association. Mitochondrial ferritin (FTMT) is a protein preferentially expressed in cells with high metabolic activity and oxygen-deprived environments, where it functions to sequester iron and mitigate oxidative damage. Fan et al. demonstrated that FTMT expression is inversely correlated with gastric cancer progression [ 32 ]. Wu et al. further revealed that FTMT expression is upregulated by hypoxia-inducible factor 1α (HIF-1α), enhancing cellular protection under hypoxic conditions[ 33 ]. Additionally, FTMT overexpression can modulate mitochondrial iron availability, potentially contributing to ineffective erythropoiesis, a process implicated in cancer progression[ 34 ]. Given that cervical cancer development is strongly associated with human papillomavirus (HPV) infection, which elevates intracellular oxidative stress, FTMT may play a protective role by reducing reactive oxygen species (ROS)-induced DNA damage and suppressing carcinogenesis. These insights align with our findings, suggesting that FTMT may protect against cervical cancer. Protein disulfide isomerase A4 (PDIA4), an endoplasmic reticulum-resident protein, is critical in protein folding and disulfide bond formation. Previous studies have reported that PDIA4 is aberrantly overexpressed in cervical cancer tissues[ 35 ]. Kaplan-Meier survival analyses indicate that elevated PDIA4 expression is associated with poorer clinical outcomes in cervical cancer patients. Functional studies have demonstrated that PDIA4 knockdown significantly suppresses the proliferation and migration of cervical cancer cells. Additionally, evidence suggests that PDIA4 may promote tumor progression by modulating apoptosis and DNA repair pathways, with its overexpression correlating with reduced survival in cervical cancer patients[ 36 ]. However, our Mendelian randomization (MR) analysis revealed an inverse association between PDIA4 expression and cervical cancer risk, contrasting with prior findings. This discrepancy underscores the need for further mechanistic studies to clarify the role of PDIA4 in cervical cancer pathogenesis. Sphingosine kinase 2 (SPHK2), one of the two isoforms of sphingosine kinase, plays a complex role in cancer biology. Previous studies have demonstrated that SPHK2 regulates intracellular sphingosine-1-phosphate (S1P) levels, influencing cellular growth, survival, and migration [ 37 ]. In cervical cancer, SPHK2 overexpression has been associated with enhanced tumor invasiveness and resistance to therapy. At the same time, its inhibition has been shown to suppress the proliferation and migration of cervical cancer cells [ 38 ]. Additionally, the pharmacological inhibition of SPHK2 has been reported to sensitize cervical cancer cells to chemotherapeutic agents, potentially overcoming treatment resistance. However, our findings suggest a protective role for SPHK2 in cervical cancer, contrasting with previous reports. This discrepancy highlights the need for further investigation into the precise mechanisms by which SPHK2 influences cervical cancer progression and therapy resistance. Transmembrane p24 transport protein 2 (TMED2) is a key regulator of vesicular protein transport within the cytoplasm. Fang et al. demonstrated that TMED2 is significantly overexpressed in breast cancer tissues at both the mRNA and protein levels[ 39 ]. Similarly, elevated TMED2 expression has been observed in head and neck squamous cell carcinoma, a biomarker for poor prognosis[ 40 ]. The oncogenic role of TMED2 is further corroborated by studies in other malignancies, including hepatocellular carcinoma, ovarian cancer, lung cancer, and chordoma[ 41 – 46 ]. However, the relationship between TMED2 and cervical cancer remains poorly characterized. Our findings suggest that TMED2 may function as a protective factor in cervical cancer, highlighting its potential as a novel therapeutic target for this disease. Rac GTPase-activating protein 1 (RACGAP1) is a specific RhoGAP that regulates Rac1 and Cdc42 signaling, thereby driving tumor progression[ 47 ]. Zhang et al. demonstrated that RACGAP1 modulates c-Jun expression via miR-192, and c-Jun, through p-JNK phosphorylation, activates AP-1, promoting the proliferation, migration, and invasion of cervical cancer cells[ 48 ]. Additionally, Ruan et al. showed that RACGAP1 Speeds up cell cycle progression by regulating CDC25C in cervical cancer cells[ 49 ]. Recent studies further revealed that FOXM1 synergizes with RACGAP1, inhibiting apoptosis mediated through the PI3K/AKT signaling pathway[ 50 ]. These findings are consistent with our results, suggesting that RACGAP1 may act as a risk factor for cervical cancer and may represent a potential therapeutic target. Our study has several strengths. First, we leveraged protein quantitative trait locus (pQTL) data from the most extensive proteomics genome-wide association study (GWAS) database, enabling a comprehensive and robust analysis. Second, the Mendelian randomization (MR) design effectively minimized confounding factors and reduced the risk of reverse causality, strengthening the validity of our causal inferences. Third, we applied false discovery rate (FDR) correction and prioritized plasma proteins with consistent odds ratio (OR) directions across analyses, further enhancing the reliability of our findings. However, several limitations should be acknowledged. First, although restricting our analysis to European populations reduced bias related to population stratification, the lack of data from other ethnic groups may limit the generalizability of our results and introduce potential biases. Second, the functional enrichment analyses were predictive, and the biological relevance of these findings requires further experimental validation. Addressing these limitations in future studies will be critical to extending the applicability and impact of our results. Despite these limitations, our study provides novel genetic insights into the molecular pathogenesis of cervical cancer and identifies promising therapeutic targets. These findings can potentially inform the development of more effective treatment strategies and ultimately improve patient clinical outcomes. Conclusion Our results indicate that DEFB135, FGL2, FTMT, PDIA4, SPHK2, and TMED2 may protect against cervical cancer, whereas RACGAP1 may promote disease pathogenesis. These findings provide a robust theoretical base for identifying potential biomarkers and therapeutic targets for cervical cancer, offering new avenues for further mechanistic and clinical investigations. Declarations Supplementary material The supplementary materials accompanying this article are provided in the associated documentation. Ethics approval and consent to participate The relevant local ethics committees approved the study protocol, and all participants provided written informed consent after receiving detailed information about the study. Additionally, this investigation was conducted by the STROBE-MR guidelines. Funding This study was supported by the following projects: General Project of the Joint Special Project of Local Universities in Yunnan Province (202001BA070001-064), Dali University Doctoral Research Start-up Fund Project (KYBS2018012), Open Project of Yunnan Provincial Key Laboratory of Entomological Biopharmaceutical R&D (AG2024002), Clinical Medicine Discipline Team Building Project of the First Affiliated Hospital of Dali University (DFYYB2024026), and Open Project of Key Laboratory of Screening and Research of Resistant Plant Resources in West Yunnan, Yunnan Province (APKL2101). General Project of the Joint Special Project of Local Universities in Yunnan Province(202101BA070001-102). Author Contribution Author contributionsYan-Hong Zhao: Formal analysis, Investigation, Resources, Visualization, Writing – original draft. Qing-Fen Ruan: data curation Jing -Hua Ning: visualization. Xin Zhang: software, visualization. Run Qu: Validation. Jing Zou: Project administration, writing-review &editing. Yi Liang: supervision.Cheng-Gui Zhang: Project administration. Yu-Zhe Zhang: conceptualization, Project administration, methodology, supervision, visualization.FundingThis study was supported by the following projects: General Project of the Joint Special Project of Local Universities in Yunnan Province (202001BA070001-064), Dali University Doctoral Research Start-up Fund Project (KYBS2018012), Open Project of Yunnan Provincial Key Laboratory of Entomological Biopharmaceutical R&D (AG2024002), Clinical Medicine Discipline Team Building Project of the First Affiliated Hospital of Dali University (DFYYB2024026), and Open Project of Key Laboratory of Screening and Research of Resistant Plant Resources in West Yunnan, Yunnan Province (APKL2101). General Project of the Joint Special Project of Local Universities in Yunnan Province（202101BA070001-102）.AcknowledgementThe views and opinions expressed in this article are solely those of the authors and do not necessarily reflect the official policies or positions of their affiliated institutions, the publisher, the editors, or the reviewers. The publisher makes no representations or warranties, either expressed or implied, regarding the accuracy or completeness of the content, including any products evaluated in the article or the claims made by their manufacturers. References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I and Jemal A (2024) Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians 74:229–263. https://doi.org/doi: 10.3322/caac.21834 Cohen PA, Jhingran A, Oaknin A and Denny L (2019) Cervical cancer. The Lancet 393:169–182. https://doi.org/doi:10.1016/s0140-6736(18)32470-x Tao T, Yang X, Qin Q, Shi W, Wang Q, Yang Y and He J (2017) NHERF1 Enhances Cisplatin Sensitivity in Human Cervical Cancer Cells. International Journal of Molecular Sciences 18. doi: 10.3390/ijms18010005 Kuehn BM (2021) WHO Launches Global Push to Eliminate Cervical Cancer. Jama 325. https://doi.org/doi: 10.1001/jama.2020.25668 Dhindsa RS, Burren OS, Sun BB, Prins BP, Matelska D, Wheeler E, Mitchell J, Oerton E, Hristova VA, Smith KR, Carss K, Wasilewski S, Harper AR, Paul DS, Fabre MA, Runz H, Viollet C, Challis B, Platt A, Ågren R, Anderson-Dring L, Atanur S, Baker D, Barrett C, Belvisi M, Bohlooly-Y M, Buvall L, Camacho N, Cazares L, Cameron-Christie S, Chen M, Cohen S, Danielson RF, Das S, Davis A, Deevi SVV, Ding W, Dougherty B, Fairhurst-Hunter Z, Garg M, Georgi B, Rangel CG, Haefliger C, Hammar M, Hanna RN, Hansen PBL, Harrow J, Henry I, Hess S, Hollis B, Hu F, Jiang X, Kundu K, Lai Z, Lal M, Lassi G, Liang Y, Lopes M, Lythgow K, MacArthur S, Maisuria-Armer M, March R, Martins C, Megy K, Menzies R, Michaëlsson E, Middleton F, Mowrey B, Muthas D, Nag A, O'Dell S, Ohne Y, Olsson H, O'Neill A, Ostridge K, Pullman B, Rae W, Raies A, Reznichenko A, Ros XR, Ryaboshapkina M, Sanganee H, Sidders B, Snowden M, Stankovic S, Stevens H, Tachmazidou I, Taiy H, Tian L, Underwood C, Walentinsson A, Wang Q-D, Zehir A, Zou Z, Vitsios D, Ashley EA, Whelan CD, Pangalos MN, Wang Q and Petrovski S (2023) Rare variant associations with plasma protein levels in the UK Biobank. Nature 622:339–347. https://doi.org/doi: 10.1038/s41586-023-06547-x Charakorn C, Thadanipon K, Chaijindaratana S, Rattanasiri S, Numthavaj P and Thakkinstian A (2018) The association between serum squamous cell carcinoma antigen and recurrence and survival of patients with cervical squamous cell carcinoma: A systematic review and meta-analysis. Gynecologic Oncology 150:190–200. https://doi.org/doi: 10.1016/j.ygyno.2018.03.056 Zhou Y, Tao L, Qiu J, Xu J, Yang X, Zhang Y, Tian X, Guan X, Cen X and Zhao Y (2024) Tumor biomarkers for diagnosis, prognosis and targeted therapy. Signal Transduction and Targeted Therapy 9. https://doi.org/doi: 10.1038/s41392-024-01823-2 Franceschi S (2021) Genomic characterisation of cervical cancer and human papillomavirus: new opportunities for precision medicine. Lancet Oncol 22:419–420. https://doi.org/doi: 10.1016/s1470-2045(21)00058-9 Li S and Gong M (2024) Drug targets for haemorrhoidal disease: proteome-wide Mendelian randomisation and colocalisation analyses. Gut 73:e31-e31. https://doi.org/doi: 10.1136/gutjnl-2023-330967 Skrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM, Swanson SA, VanderWeele TJ, Higgins JPT, Timpson NJ, Dimou N, Langenberg C, Golub RM, Loder EW, Gallo V, Tybjaerg-Hansen A, Davey Smith G, Egger M and Richards JB (2021) Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement. JAMA 326:1614–1621. https://doi.org/doi: 10.1001/jama.2021.18236 Yu Y, Hou L, Shi X, Sun X, Liu X, Yu Y, Yuan Z, Li H and Xue F (2022) Impact of nonrandom selection mechanisms on the causal effect estimation for two-sample Mendelian randomization methods. PLoS Genet 18:e1010107. https://doi.org/doi: 10.1371/journal.pgen.1010107 Lin J, Zhou J and Xu Y (2023) Potential drug targets for multiple sclerosis identified through Mendelian randomization analysis. Brain 146:3364–3372. https://doi.org/doi: 10.1093/brain/awad070 Xu D, Yang X, Wu W and Yang J (2024) Identification of Novel Protein Biomarkers and Drug Targets for Acne Vulgaris by Integrating Human Plasma Proteome with Genome-Wide Association Data. J Inflamm Res 17:4431–4441. doi: 10.2147/JIR.S463450 Song N, Shi P, Cui K, Zeng L, Wang Z, Di W, Li J, Fan Y, Li Z, Zhang J, Su W and Wang H (2024) Potential drug targets for tumors identified through Mendelian randomization analysis. Scientific Reports 14. https://doi.org/doi: 10.1038/s41598-024-62178-w Ferkingstad E, Sulem P, Atlason BA, Sveinbjornsson G, Magnusson MI, Styrmisdottir EL, Gunnarsdottir K, Helgason A, Oddsson A, Halldorsson BV, Jensson BO, Zink F, Halldorsson GH, Masson G, Arnadottir GA, Katrinardottir H, Juliusson K, Magnusson MK, Magnusson OT, Fridriksdottir R, Saevarsdottir S, Gudjonsson SA, Stacey SN, Rognvaldsson S, Eiriksdottir T, Olafsdottir TA, Steinthorsdottir V, Tragante V, Ulfarsson MO, Stefansson H, Jonsdottir I, Holm H, Rafnar T, Melsted P, Saemundsdottir J, Norddahl GL, Lund SH, Gudbjartsson DF, Thorsteinsdottir U and Stefansson K (2021) Large-scale integration of the plasma proteome with genetics and disease. Nat Genet 53:1712–1721. https://doi.org/doi: 10.1038/s41588-021-00978-w Bowden J, Del Greco MF, Minelli C, Davey Smith G, Sheehan NA and Thompson JR (2016) Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic. Int J Epidemiol 45:1961–1974. https://doi.org/doi: 10.1093/ije/dyw220 Meng Y, Tan Z, Su Y, Li L and Chen C (2023) Causal association between common rheumatic diseases and glaucoma: a Mendelian randomization study. Front Immunol 14:1227138. https://doi.org/doi: 10.3389/fimmu.2023.1227138 Zhang L, Zhang C, Zhang J, Liu A, Wang P and Xu J (2023) A Bidirectional Mendelian Randomization Study of Sarcopenia-Related Traits and Knee Osteoarthritis. Clin Interv Aging 18:1577–1586. doi: 10.2147/CIA.S424633 Pierce BL and Burgess S (2013) Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators. Am J Epidemiol 178:1177–84. https://doi.org/doi: 10.1093/aje/kwt084 Bowden J, Davey Smith G, Haycock PC and Burgess S (2016) Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol 40:304–14. https://doi.org/doi: 10.1002/gepi.21965 Bowden J, Davey Smith G and Burgess S (2015) Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol 44:512–25. https://doi.org/doi: 10.1093/ije/dyv080 Cohen JF, Chalumeau M, Cohen R, Korevaar DA, Khoshnood B and Bossuyt PM (2015) Cochran's Q test was useful to assess heterogeneity in likelihood ratios in studies of diagnostic accuracy. J Clin Epidemiol 68:299–306. https://doi.org/doi: 10.1016/j.jclinepi.2014.09.005 Verbanck M, Chen CY, Neale B and Do R (2018) Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 50:693–698. https://doi.org/doi: 10.1038/s41588-018-0099-7 Hemani G, Tilling K and Davey Smith G (2017) Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet 13:e1007081. doi: 10.1371/journal.pgen.1007081 Bulgay C, Kasakolu A, Bıyıklı T, Koncagul S, Kazan HH, Ahmetov II, Ergun MA, Griffiths MD and Szabo A (2025) Genome-Wide Association Study of Exercise Addiction Among Elite Wrestlers. Brain Sciences 15. https://doi.org/doi: 10.3390/brainsci15020102 Yan J, Zhao Q, Gabrusiewicz K, Kong L-Y, Xia X, Wang J, Ott M, Xu J, Davis RE, Huo L, Rao G, Sun S-C, Watowich SS, Heimberger AB and Li S (2019) FGL2 promotes tumor progression in the CNS by suppressing CD103 + dendritic cell differentiation. Nature Communications 10. https://doi.org/doi: 10.1038/s41467-018-08271-x Shalev I, Liu H, Koscik C, Bartczak A, Javadi M, Wong KM, Maknojia A, He W, Liu MF, Diao J, Winter E, Manuel J, McCarthy D, Cattral M, Gommerman J, Clark DA, Phillips MJ, Gorczynski RR, Zhang L, Downey G, Grant D, Cybulsky MI and Levy G (2008) Targeted Deletion of fgl2 Leads to Impaired Regulatory T Cell Activity and Development of Autoimmune Glomerulonephritis. The Journal of Immunology 180:249–260. https://doi.org/doi: 10.4049/jimmunol.180.1.249 Melnyk MC, Shalev I, Zhang J, Bartczak A, Gorczynski RM, Selzner N, Inman R, Marsden PA, Phillips MJ, Clark DA and Levy GA (2011) The prothrombinase activity of FGL2 contributes to the pathogenesis of experimental arthritis. Scandinavian Journal of Rheumatology 40:269–278. https://doi.org/doi: 10.3109/03009742.2010.536163 Yan J, Zhao Q, Wang J, Tian X, Wang J, Xia X, Ott M, Rao G, Heimberger AB and Li S (2021) FGL2-wired macrophages secrete CXCL7 to regulate the stem-like functionality of glioma cells. Cancer Letters 506:83–94. https://doi.org/doi: 10.1016/j.canlet.2021.02.021 Latha K, Yan J, Yang Y, Gressot LV, Kong L-Y, Manyam G, Ezhilarasan R, Wang Q, Sulman EP, Eric Davis R, Huang S, Fuller GN, Rao A, Heimberger AB, Li S and Rao G (2019) The Role of Fibrinogen-Like Protein 2 on Immunosuppression and Malignant Progression in Glioma. JNCI: Journal of the National Cancer Institute 111:292–300. https://doi.org/doi: 10.1093/jnci/djy107 Liu Y, Xu L, Zeng Q, Wang J, Wang M, Xi D, Wang X, Yang D, Luo X and Ning Q (2012) Downregulation of FGL2/prothrombinase delays HCCLM6 xenograft tumour growth and decreases tumour angiogenesis. Liver International 32:1585–1595. https://doi.org/doi: 10.1111/j.1478-3231.2012.02865.x Fan W, Wu Z, Xu S, Liu Z, Huang Y and Wang P (2025) Mendelian randomization analysis of plasma proteins reveals potential novel tumor markers for gastric cancer. Scientific Reports 15. https://doi.org/doi: 10.1038/s41598-025-88118-w Wu Q, Wu W-S, Su L, Zheng X, Wu W-Y, Santambrogio P, Gou Y-J, Hao Q, Wang P-N, Li Y-R, Zhao B-L, Nie G, Levi S and Chang Y-Z (2019) Mitochondrial Ferritin Is a Hypoxia-Inducible Factor 1α-Inducible Gene That Protects from Hypoxia-Induced Cell Death in Brain. Antioxidants & Redox Signaling 30:198–212. https://doi.org/doi: 10.1089/ars.2017.7063 Invernizzi R, Travaglino E, Della Porta MG, Gallì A, Malcovati L, Rosti V, Bergamaschi G, Erba BG, Bellistri F, Bastia R, Santambrogio P, Levi S and Cazzola M (2013) Effects of mitochondrial ferritin overexpression in normal and sideroblastic erythroid progenitors. British Journal of Haematology 161:726–737. https://doi.org/doi: 10.1111/bjh.12316 Xing F, Song Z and Cheng Z (2022) High expression of PDIA4 promotes malignant cell behavior and predicts reduced survival in cervical cancer. Oncology Reports 48. doi: 10.3892/or.2022.8399 Zhang Y, Li T, Zhang L, Shangguan F, Shi G, Wu X, Cui Y, Wang Xe, Wang X, Liu Y, Lu B, Wei T, Wang C-c and Wang L (2019) Targeting the functional interplay between endoplasmic reticulum oxidoreductin-1α and protein disulfide isomerase suppresses the progression of cervical cancer. EBioMedicine 41:408–419. https://doi.org/doi: 10.1016/j.ebiom.2019.02.041 Zhang L, Urtz N, Gaertner F, Legate KR, Petzold T, Lorenz M, Mazharian A, Watson SP and Massberg S (2013) Sphingosine kinase 2 (Sphk2) regulates platelet biogenesis by providing intracellular sphingosine 1-phosphate (S1P). Blood 122:791–802. https://doi.org/doi: 10.1182/blood-2012-12-473884 Zhang Y, Cheng L, Shi X, Song Y, Chen X-y, Chen M-b, Yao J, Zhang Z-q and Cai S (2022) The sphingosine kinase inhibitor SKI-V suppresses cervical cancer cell growth. International Journal of Biological Sciences 18:2994–3005. https://doi.org/doi: 10.7150/ijbs.71381 Fang Z, Song Y-X, Wo G-Q, Zhou H-L, Li L, Yang S-Y, Chen X, Zhang J and Tang J-H (2022) Screening of the novel immune-suppressive biomarkers of TMED family and whether knockdown of TMED2/3/4/9 inhibits cell migration and invasion in breast cancer. Annals of Translational Medicine 10:1280–1280. https://doi.org/doi: 10.21037/atm-22-5444 Gao W, Zhang Z-W, Wang H-Y, Li X-D, Peng W-T, Guan H-Y, Liao Y-X and Liu A (2022) TMED2/9/10 Serve as Biomarkers for Poor Prognosis in Head and Neck Squamous Carcinoma. Frontiers in Genetics 13. https://doi.org/doi: 10.3389/fgene.2022.895281 Zheng H, Yang Y, Han J, Jiang W-h, Chen C, Wang M-c, Gao R, Li S, Tian T, Wang J, Ma L-j, Ren H and Zhou W-p (2016) TMED3 promotes hepatocellular carcinoma progression via IL-11/STAT3 signaling. Scientific Reports 6. https://doi.org/doi: 10.1038/srep37070 Xie A, Xu X, Kuang P, Zhang L and Yu F (2021) TMED3 promotes the progression and development of lung squamous cell carcinoma by regulating EZR. Cell Death & Disease 12. https://doi.org/doi: 10.1038/s41419-021-04086-9 Yang Y-C, Chien M-H, Lai T-C, Tung M-C, Jan Y-H, Chang W-M, Jung S-M, Chen M-H, Yeh C-N and Hsiao M (2021) Proteomics-based identification of TMED9 is linked to vascular invasion and poor prognoses in patients with hepatocellular carcinoma. Journal of Biomedical Science 28. https://doi.org/doi: 10.1186/s12929-021-00727-5 Zhang X, Hao HH, Zhuang HW, Wang J, Sheng Y, Xu F, Dou J, Chen C and Shen Y (2020) Circular RNA circ_0008305 aggravates hepatocellular carcinoma growth through binding to miR-186 and inducing TMED2. Journal of Cellular and Molecular Medicine 26:1742–1753. https://doi.org/doi: 10.1111/jcmm.15945 Shi-Peng G, Chun-Lin C, Huan W, Fan-Liang M, Yong-Ning C, Ya-Di Z, Guang-Ping Z and Ye-Ping C (2017) TMED2 promotes epithelial ovarian cancer growth. Oncotarget 8:94151–94165. https://doi.org/doi: 10.18632/oncotarget.21593 Yang J, Huang H, Xiao D, Duan Y, Zheng Y and Chen Z (2021) Knockdown of TMED3 inhibits cell viability and migration and increases apoptosis in human chordoma cells. International Journal of Oncology 58. https://doi.org/doi: 10.3892/ijo.2021.5195 Touré A, Dorseuil O, Morin L, Timmons P, Jégou B, Reibel L and Gacon G (1998) MgcRacGAP, A New Human GTPase-activating Protein for Rac and Cdc42 Similar to Drosophila rotundRacGAP Gene Product, Is Expressed in Male Germ Cells. Journal of Biological Chemistry 273:6019–6023. https://doi.org/doi: 10.1074/jbc.273.11.6019 Zhang T, Wang C, Wang K, Liang Y, Liu T, Feng L and Yang X (2022) RacGAP1 promotes the malignant progression of cervical cancer by regulating AP-1 via miR-192 and p-JNK. Cell Death & Disease 13. https://doi.org/doi: 10.1038/s41419-022-05036-9 Ruan X and Jiang J (2022) RACGAP1 promotes proliferation and cell cycle progression by regulating CDC25C in cervical cancer cells. Tissue and Cell 76. https://doi.org/doi: 10.1016/j.tice.2022.101804 Yao H, Li J, Zhou D, Pan X, Chu Y and Yin J (2024) FOXM1 transcriptional regulation of RacGAP1 activates the PI3K/AKT signaling pathway to promote the proliferation, migration, and invasion of cervical cancer cells. International Journal of Clinical Oncology 29:333–344. https://doi.org/doi: 10.1007/s10147-023-02452-5 Additional Declarations No competing interests reported. Supplementary Files Supplementaryfile.zip Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 24 Nov, 2025 Reviews received at journal 24 Sep, 2025 Reviewers agreed at journal 24 Sep, 2025 Reviewers agreed at journal 08 May, 2025 Reviewers invited by journal 07 May, 2025 Editor assigned by journal 06 May, 2025 Submission checks completed at journal 06 May, 2025 First submitted to journal 28 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6551278","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":455578914,"identity":"0991e165-fb0b-4626-9026-06782ec9f363","order_by":0,"name":"Yan-Hong Zhao","email":"","orcid":"","institution":"Dali University","correspondingAuthor":false,"prefix":"","firstName":"Yan-Hong","middleName":"","lastName":"Zhao","suffix":""},{"id":455578915,"identity":"75e83f8e-40d2-4643-8845-f2014b634be3","order_by":1,"name":"Qing-Fen Ruan","email":"","orcid":"","institution":"The First Affiliated Hospital of Dali University","correspondingAuthor":false,"prefix":"","firstName":"Qing-Fen","middleName":"","lastName":"Ruan","suffix":""},{"id":455578916,"identity":"3d4379cf-3211-45fe-b272-c6e53888c9e0","order_by":2,"name":"Jing-Hua Ning","email":"","orcid":"","institution":"Dali University","correspondingAuthor":false,"prefix":"","firstName":"Jing-Hua","middleName":"","lastName":"Ning","suffix":""},{"id":455578917,"identity":"80448acb-57fa-4848-8c27-2be36bde649b","order_by":3,"name":"Xin Zhang","email":"","orcid":"","institution":"Dali University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Zhang","suffix":""},{"id":455578918,"identity":"0d0b7500-33be-4a77-a183-1d1eb4f92950","order_by":4,"name":"Run Qu","email":"","orcid":"","institution":"Dali University","correspondingAuthor":false,"prefix":"","firstName":"Run","middleName":"","lastName":"Qu","suffix":""},{"id":455578919,"identity":"ae143182-633d-4505-a3f8-1279e364474a","order_by":5,"name":"Jing Zou","email":"","orcid":"","institution":"The First Affiliated Hospital of Dali University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Zou","suffix":""},{"id":455578920,"identity":"aa8e649a-20f8-4bbc-ae39-7eaa3ff20fc8","order_by":6,"name":"Yi Liang","email":"","orcid":"","institution":"University Health Network","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Liang","suffix":""},{"id":455578921,"identity":"2b3cd649-55f0-4328-8a25-c0ae32bc4633","order_by":7,"name":"Cheng-gui Zhang","email":"","orcid":"","institution":"Dali University","correspondingAuthor":false,"prefix":"","firstName":"Cheng-gui","middleName":"","lastName":"Zhang","suffix":""},{"id":455578922,"identity":"d5707341-d139-4052-8722-50acf1442d0a","order_by":8,"name":"Yu-Zhe Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYBACPgglUc/P3nwMzGRjJ6CFDUJZJEj2HEtjYEgAijATp6UiwWBGjhlYCwNBLfxnjD/z/JHIM+A58+3Bxx/b5PmYGRg/fMzBZ8sZA2MeHolic/be7YYzEm4btjEzMEvO3IZHC2OPQTKPhATjzp6z26R5Em4zArWwMfPi08LMY3CYx0CCccONnGcgLfaEtbDxGDbzJEgkArWwgbQkEtbCw1bMOOeAhDEwkM0kZ6TdTm5jZmzG6xd+/sObP7z5UycHjMpnEh9sbtvOb28++OEjHi0gwMSDymdswK8epOQHQSWjYBSMglEwogEA8ZlHhavnZWMAAAAASUVORK5CYII=","orcid":"","institution":"Dali University","correspondingAuthor":true,"prefix":"","firstName":"Yu-Zhe","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-04-29 00:53:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6551278/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6551278/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82643861,"identity":"f654f58e-8b8f-41e0-a638-3f90f0dd7eda","added_by":"auto","created_at":"2025-05-13 15:43:17","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":21471,"visible":true,"origin":"","legend":"\u003cp\u003eFundamental assumptions of Mendelian randomization (MR) analysis. Relevance assumption 1: Instrumental variables (IVs) must strongly associate with the exposure. Independence assumption 2: IVs must be independent of confounding factors influencing the exposure and the outcome. Exclusion restriction assumption 3: IVs must affect the outcome exclusively through exposure, with no alternative pathways.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6551278/v1/57d5e74e704ef7a8d581a36b.jpg"},{"id":82643865,"identity":"f2bbcca9-00de-4ca1-948a-8a73d4f6656e","added_by":"auto","created_at":"2025-05-13 15:43:17","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":40945,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study design\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6551278/v1/05d10b918b454b2803f0446d.jpg"},{"id":82643863,"identity":"cf154ca6-441b-4aa0-9db7-cef827f5cc9b","added_by":"auto","created_at":"2025-05-13 15:43:17","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":150452,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot showing the results of MR of the plasma proteins. Low: Plasma protein phenotype associated with a protective effect against cervical cancer (CC); Not: Plasma protein phenotype showing no significant causal impact on CC; High: Plasma protein phenotype associated with an increased risk of CC.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6551278/v1/163856f92d359234b6c37c73.jpg"},{"id":82644557,"identity":"66b13280-759b-4ac8-b200-99c2644a0601","added_by":"auto","created_at":"2025-05-13 15:51:17","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":168590,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots illustrating the causal relationships between plasma protein phenotypes and cervical cancer (CC). Odds ratio (OR): An OR \u0026gt; 1 indicates a positive association between the plasma protein phenotype and CC risk, while an OR \u0026lt; 1 suggests a protective effect.\u003cstrong\u003e \u003c/strong\u003eConfidence interval (CI): The 95% CI reflects the precision of the effect estimate.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6551278/v1/be747231f3cd053d8ba7ce1b.jpg"},{"id":82643870,"identity":"3b4b7735-1d92-47c1-a255-bfd80e380560","added_by":"auto","created_at":"2025-05-13 15:43:17","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":367598,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots of Mendelian randomization (MR) analysis showing the association between seven plasma proteins (DEFB135, FGL2, FTMT, PDIA4, SPHK2, TMED2, and RACGAP1) and cervical cancer (CC). X-axis: Genetic association with plasma protein levels. Y-axis: Genetic association with CC risk. The five lines represent the results from five distinct MR methods. Each black dot corresponds to a single nucleotide polymorphism (SNP) used as an instrumental variable.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6551278/v1/29a21df3f5ae1340f2151be2.jpg"},{"id":82645630,"identity":"20fc3767-d9dd-4c81-9c77-77ec8d09b45c","added_by":"auto","created_at":"2025-05-13 15:59:17","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":140109,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots illustrating the reverse causal relationships between plasma protein phenotypes and cervical cancer (CC). Odds ratio (OR): An OR \u0026gt; 1 indicates a positive association between the plasma protein phenotype and CC risk, while an OR \u0026lt; 1 suggests a protective effect. Confidence interval (CI): The 95% CI reflects the precision of the effect estimate.\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6551278/v1/02632214994442a06ceaf6dd.jpg"},{"id":82643873,"identity":"e660613c-3154-4ce4-85a3-0afa2bb8002f","added_by":"auto","created_at":"2025-05-13 15:43:17","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":373568,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of seven protein-coding genes associated with cervical cancer. (\u003cstrong\u003eA\u003c/strong\u003e) Bar chart of GO enrichment across biological processes (BP), cellular components (CC), and molecular functions (MF). The bar color represents the p-value, and \"Count\" indicates the number of enriched genes. (\u003cstrong\u003eB\u003c/strong\u003e) Bubble chart of GO enrichment. The bubble color corresponds to the p-value, and the bubble size represents the \"GeneRatio,\" defined as the proportion of enriched genes relative to the total number of genes analyzed. (\u003cstrong\u003eC\u003c/strong\u003e) Bar chart showing KEGG pathway enrichment. The bar color represents the p-value, and \"Count\" indicates the number of enriched genes per pathway. (\u003cstrong\u003eD\u003c/strong\u003e) Bubble chart of KEGG enrichment. The bubble color corresponds to the p-value, and the bubble size reflects the number of enriched genes.\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6551278/v1/6325284d8de0ac53401f22e1.jpg"},{"id":82643883,"identity":"effe6c3a-2360-474d-bd42-b81ae30441aa","added_by":"auto","created_at":"2025-05-13 15:43:18","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":311344,"visible":true,"origin":"","legend":"\u003cp\u003eGeneMANIA network analysis of the seven plasma protein-coding genes. The network illustrates functional relationships among these genes, predominantly characterized by co-expression (92.52%) and predicted interactions (7.48%).\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6551278/v1/dc6c69fb7dd8bbb4cc0b9011.jpg"},{"id":82646078,"identity":"a31e832b-466a-4f35-ad1f-cef3c751d6d2","added_by":"auto","created_at":"2025-05-13 16:07:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2365854,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6551278/v1/fd9cee4c-3e36-4bcc-ad10-00e5e2bd482d.pdf"},{"id":82644558,"identity":"16371e16-6c7b-4645-bf9e-666e7b4acdad","added_by":"auto","created_at":"2025-05-13 15:51:17","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":470213,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile.zip","url":"https://assets-eu.researchsquare.com/files/rs-6551278/v1/f4905e36caa50b3dc39f2b63.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bidirectional Mendelian Randomization identifies plasma proteins associated with Cervical Cancer risk","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCervical cancer is the leading cause of cancer-related deaths among women worldwide. Recent epidemiological data from the International Agency for Research on Cancer (IARC) estimates its incidence and mortality rates are particularly significant among common female cancers[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The primary risk factor for cervical cancer is persistent infection with high-risk subtypes of human papillomavirus (HPV). The E6 and E7 proteins of HPV promote the malignant transformation of cells and tumorigenesis by interacting with host cell proteins[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Current therapeutic strategies for cervical cancer rely heavily on platinum-based chemotherapeutic agents, such as cisplatin; however, the emergence of drug resistance frequently compromises treatment efficacy and contributes to disease recurrence[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The World Health Organization (WHO) emphasizes that early detection and timely intervention are critical for reducing the burden of cervical cancer[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, Screening for potential targets to combat cervical cancer is essential.\u003c/p\u003e \u003cp\u003eIn recent years, plasma proteins have emerged as significant targets for drug development due to their ability to diagnose and predict diseases, identify therapeutic targets, and elucidate the pathophysiology of various conditions[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Previous studies have demonstrated the high independent diagnostic value of squamous cell carcinoma antigen (SCCA) as a tumor biomarker for cervical cancer[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. CA125 has been recognized as a biomarker for the diagnosis and prognosis of cervical cancer[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Furthermore, A study based on the UK Biobank cohort found that the PAX8, CLPTM1L, and HLA genes play a role in cervical carcinogenesis[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, traditional observational studies are often limited by confounding factors and reverse causality, leading to inconclusive findings. To address these limitations. Recent research has integrated protein quantitative trait loci (pQTLs) into Mendelian randomization (MR) analyses, enabling the prioritization of drug targets with enhanced causal inference[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Mendelian randomization utilizes single nucleotide polymorphisms (SNPs) as instrumental variables to infer causal relationships between exposure factors and outcomes[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], minimizing bias from confounding factors and avoiding interference from reverse causation[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWith the rapid advancement of high-throughput proteomics techniques in plasma analysis, researchers have established a causal relationship between plasma proteins and various diseases, including multiple sclerosis, acne, and breast cancer, through Mendelian Randomization (MR) analysis[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, the causal relationship between plasma proteins and cervical cancer remains poorly understood. Therefore, we leveraged the largest available plasma proteomics dataset to perform a comprehensive two-sample bidirectional MR analysis, examining the causal effects of 4,907 plasma proteins on cervical cancer risk. Our findings provide novel insights into the molecular mechanisms underlying cervical cancer and establish a theoretical base for advancing early Screening, diagnostic strategies, and therapeutic interventions for this disease.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThe study design is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Large-scale proteomics data were analyzed using R software (v 4.3.2). We employed a two-sample bidirectional Mendelian randomization (MR) method to investigate the causal relationship between 4,907 plasma proteins and cervical cancer. Furthermore, we conducted extensive sensitivity analyses to ensure our findings were robust and reliable. Finally, functional enrichment and GeneMANIA analyses were performed on the plasma protein-coding genes most significantly associated with cervical cancer. MR analysis relies on three fundamental assumptions: (a) the relevance assumption, instrumental variables (IVs) must be closely related to the exposure factors; (b) the independence assumption, instrumental variables should be independent of any confounders related to both the exposure and the outcome; and (c) the exclusion restriction assumption: instrumental variables should only influence the outcome through the exposure factors, rather than acting directly on the outcome (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Sources\u003c/h3\u003e\n\u003cp\u003eThe exposure data utilized in this study were obtained from a publicly accessible proteomics genome-wide association study (GWAS) dataset available through the deCODE database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.decode.com\u003c/span\u003e\u003cspan address=\"https://www.decode.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This dataset is derived from a comprehensive protein quantitative trait loci (pQTL) analysis conducted on a cohort of 35,559 Icelandic individuals, from which 4,907 plasma proteins were identified (Supplemental Table\u0026nbsp;1)[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGenome-wide association study (GWAS) data for cervical cancer (GWAS ID: finngen_R10_C3_CERVIX_UTERI_EXALLC) were obtained from the Finnish database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.finngen.fi/en/access_results\u003c/span\u003e\u003cspan address=\"https://www.finngen.fi/en/access_results\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Specifically, we utilized cohort data labeled as \"Malignant neoplasm of the cervix (controls excluding all cancers),\" which included 388 cases and 182,927 controls (R10) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (Supplemental Table\u0026nbsp;2). To minimize bias due to population heterogeneity, we restricted the analysis to individuals of European descent[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Additionally, the relevant ethical committees approved all data used in this study, and no further ethical approvals were necessary.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe data sources utilized for the Mendelian randomization analysis within the study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDatabase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlasma protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edeCODE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIcelander\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinnGen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eSelection of Instrumental Variables\u003c/h3\u003e\n\u003cp\u003eTo identify valid instrumental variables (IVs), we implemented rigorous selection criteria using the TwoSampleMR package in R (v 4.3.2). First, to satisfy the relevance assumption of Mendelian randomization (MR) analysis, we selected single nucleotide polymorphisms (SNPs) that were closely associated with the exposure, applying a threshold of \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;5\u0026times;10^\u003csup\u003e\u0026minus;8\u003c/sup\u003e [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Second, to address potential linkage disequilibrium (LD), we performed SNP clumping with 10,000 kb and r2\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Third, to minimize bias from weak instruments, we calculated the F-statistic for each SNP using the formula (F\u0026thinsp;=\u0026thinsp;R^2*(n-2)/(1-R^2); R^2\u0026thinsp;=\u0026thinsp;2*(1-MAF) *MAF*beta^2/(2*(1-MAF) *MAF*beta^2\u0026thinsp;+\u0026thinsp;2*(1-MAF) *MAF*se^2*n)). Only IVs with an F-statistic\u0026thinsp;\u0026gt;\u0026thinsp;10 were retained for downstream analyses. Finally, we excluded SNPs with palindromic structures when harmonizing exposure and outcome data using the harmonise_data function[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. For reverse MR analysis, we applied a significance threshold of \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;5\u0026times;10^\u003csup\u003e\u0026minus;6\u003c/sup\u003e to identify SNPs strongly associated with cervical cancer (CC). In contrast, for forward MR analysis, a threshold of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10^\u003csup\u003e\u0026minus;8\u003c/sup\u003e was sufficient to ensure adequate plasma protein IVs.\u003c/p\u003e\n\u003ch3\u003eForward Mendelian Randomization Analysis\u003c/h3\u003e\n\u003cp\u003eTo assess the causal relationship between 4,907 plasma proteins and cervical cancer, we applied five distinct Mendelian randomization (MR) methods. The inverse variance weighted (IVW) method integrates the Wald ratio estimates of the causal effects of various single nucleotide polymorphisms (SNPs) to assess the causal relationship between exposure and outcome, making it the predominant analytic method[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The weighted median approach is more robust against invalid instrumental variables and provides reliable estimates when valid instrumental variables account for more than 50% of the weight[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The MR-Egger regression method evaluates invalid causal hypotheses and ensures consistency in assessing causality, particularly in cases where instrumental variables exhibit insufficient genetic variation[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The weighted mode effectively captures the most representative causal relationships by emphasizing the effect values of the plurality, especially when multiple instrumental variables are involved. Additionally, the simple mode is another analytical method based on the effects of plurality, which is more resilient to extreme values and less influenced by small amounts of outlier data. When different analytical methods yield inconsistent results, the IVW method should be prioritized.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eSensitivity Analysis\u003c/h3\u003e\n\u003cp\u003eSensitivity analyses were conducted to ensure the robustness and reliability of our findings. These included Cochran's Q test, MR-Egger intercept, MR-PRESSO, and leave-one-out analysis. The heterogeneity of the findings was analyzed using Cochran's Q test; a \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicates the absence of heterogeneity[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The pleiotropy of relationships between instrumental variables and other potential confounders was assessed through the MR-Egger method. A result of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the MR-Egger intercept analysis suggests the presence of horizontal pleiotropy. Additionally, the MR-PRESSO method was explicitly employed to identify outlier SNPs that exhibited horizontal pleiotropy, provided that more than 50% of the instrumental variables were valid[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These comprehensive sensitivity analyses collectively strengthened the validity of our causal inferences.\u003c/p\u003e \u003cp\u003eFurthermore, we conducted a leave-one-out analysis, which reassessed the effect values of the remaining SNPs by sequentially removing each SNP. This approach aimed to validate the results' reliability and robustness by evaluating each SNP's impact on the findings. Scatter plots were generated to visually represent the causal relationships between genetic instruments and outcomes. Additionally, forest plots were employed to display effect sizes and their corresponding statistical significance.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eReverse Mendelian Randomization Analysis\u003c/h2\u003e \u003cp\u003eTo further elucidate the causal relationship between exposure and outcome, we conducted a reverse Mendelian randomization (MR) analysis along with a sensitivity analysis, using cervical cancer (CC) as the exposure factor and plasma proteins associated with CC, identified in the forward MR analysis, as the outcome. This approach facilitated the identification of potential reverse causality, mitigated the effects of confounding factors, and ensured the robustness of our study. The analytical and statistical methods employed were as previously described[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGO/KEGG Pathway Enrichment and GeneMANIA Analysis\u003c/h3\u003e\n\u003cp\u003eTo elucidate the biological relevance of the identified plasma proteins in cervical cancer (CC), we performed functional annotation and pathway enrichment analyses using the clusterProfiler package (V 4.10.1) in R (v 4.3.2). Gene Ontology (GO) analysis was conducted to categorize the biomarker genes into three functional domains: biological processes (BP), molecular functions (MF), and cellular components (CC). Concurrently, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was employed to identify metabolic and signaling pathways associated with these genes. A significance threshold of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was applied for both GO and KEGG analyses. Furthermore, to explore potential protein-protein interactions, we constructed a protein-protein interaction (PPI) network using GeneMANIA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://genemania.org/\u003c/span\u003e\u003cspan address=\"http://genemania.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), providing insights into the functional relationships among the identified biomarkers.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eForward MR analysis\u003c/h2\u003e \u003cp\u003eFirst, we selected exposure single nucleotide polymorphisms (SNPs) at a genome-wide significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10^\u003csup\u003e\u0026minus;8\u003c/sup\u003e, identifying 14,475 SNPs linked to plasma proteins as instrumental variables (IVs). We then performed a two-sample Mendelian randomization (MR) analysis for each of the 4,907 plasma proteins using five distinct analytical methods. The results were further refined using the inverse variance weighted (IVW) method, applying a significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and a false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.2. Ultimately, we identified seven plasma protein phenotypes significantly associated with CC: DEFB135, FGL2, FTMT, PDIA4, SPHK2, TMED2, and RACGAP1.\u003c/p\u003e \u003cp\u003eThe results of the Inverse Variance Weighting (IVW) analysis indicated that DEFB135 (OR\u0026thinsp;=\u0026thinsp;0.201, 95% CI\u0026thinsp;=\u0026thinsp;0.082\u0026ndash;0.492, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), FGL2 (OR\u0026thinsp;=\u0026thinsp;0.104, 95% CI\u0026thinsp;=\u0026thinsp;0.032\u0026ndash;0.338, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), FTMT (OR\u0026thinsp;=\u0026thinsp;0.612, 95% CI\u0026thinsp;=\u0026thinsp;0.465\u0026ndash;0.804, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), PDIA4 (OR\u0026thinsp;=\u0026thinsp;0.088, 95% CI\u0026thinsp;=\u0026thinsp;0.026\u0026ndash;0.295, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), SPHK2 (OR\u0026thinsp;=\u0026thinsp;0.102, 95% CI\u0026thinsp;=\u0026thinsp;0.030\u0026ndash;0.350, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and TMED2 (OR\u0026thinsp;=\u0026thinsp;0.045, 95% CI\u0026thinsp;=\u0026thinsp;0.008\u0026ndash;0.246, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were negatively correlated with colorectal cancer (CC), suggesting a potential protective effect. Conversely, RACGAP1 (OR\u0026thinsp;=\u0026thinsp;1.755, 95% CI\u0026thinsp;=\u0026thinsp;1.286\u0026ndash;2.395, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) exhibited a positive correlation with CC, indicating its possible role in disease pathogenesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These findings were further supported by complementary analyses, including MR-Egger regression, weighted median (WM), and both simple and weighted mode methods. To enhance the robustness of our results, we generated volcano plots to visualize the strength and significance of the associations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eThe robustness of our findings was validated through sensitivity analyses, including heterogeneity assessment using Cochran's Q test and evaluation of horizontal pleiotropy via the MR-Egger intercept test. The Cochran's Q test revealed that the p-values for the seven plasma proteins associated with cervical cancer (CC) were greater than 0.05, indicating no significant heterogeneity. Similarly, the p-values from the Egger-intercept test were also above 0.05, suggesting that they were not influenced by horizontal pleiotropy (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Further validation using the MR-PRESSO method confirmed the reliability of the Mendelian randomization (MR) results. Additionally, the leave-one-out method analysis demonstrated that our findings were not dependent on any single SNP (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eReverse Mendelian Randomization Analysis\u003c/h2\u003e \u003cp\u003eTo assess potential reverse causality between the seven identified plasma proteins and cervical cancer (CC), we performed an inverse Mendelian randomization (MR) analysis. This analysis included the inverse variance weighted (IVW) method, MR-Egger regression, weighted median, simple mode, and weighted mode. The IVW results indicated no significant causal effects of the plasma proteins on CC risk: TMED2 (OR\u0026thinsp;=\u0026thinsp;0.956, 95% CI\u0026thinsp;=\u0026thinsp;0.909\u0026ndash;1.005, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.075), FGL2 (OR\u0026thinsp;=\u0026thinsp;0.954, 95% CI\u0026thinsp;=\u0026thinsp;0.885\u0026ndash;1.029, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.223), SPHK2 (OR\u0026thinsp;=\u0026thinsp;0.955, 95% CI\u0026thinsp;=\u0026thinsp;0.896\u0026ndash;1.018, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.156), DEFB135 (OR\u0026thinsp;=\u0026thinsp;0.950, 95% CI\u0026thinsp;=\u0026thinsp;0.898\u0026ndash;1.006, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.080), PDIA4 (OR\u0026thinsp;=\u0026thinsp;0.948, 95% CI\u0026thinsp;=\u0026thinsp;0.887\u0026ndash;1.014, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.121), FTMT (OR\u0026thinsp;=\u0026thinsp;0.873, 95% CI\u0026thinsp;=\u0026thinsp;0.724\u0026ndash;1.054, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.157), and RACGAP1 (OR\u0026thinsp;=\u0026thinsp;1.083, 95% CI\u0026thinsp;=\u0026thinsp;0.957\u0026ndash;1.225, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.205). Similarly, none of the supplementary MR methods yielded statistically significant causal estimates (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), providing no evidence to support reverse causality between these plasma proteins and CC.\u003c/p\u003e \u003cp\u003eSensitivity analysis of the reverse MR revealed Cochran's Q test \u003cem\u003ep\u003c/em\u003e-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05, likely attributable to genetic variation influencing phenotypes and population-specific genetic backgrounds, potentially mediated through multiple pathways. However, the remaining plasma protein phenotypes showed no evidence of heterogeneity or horizontal pleiotropy, further supporting the robustness of our findings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGO/KEGG Pathway Enrichment and GeneMANIA Analysis\u003c/h2\u003e \u003cp\u003eIn the GO analysis, the disease-associated plasma proteins were significantly enriched in specific biological processes (BP), cellular components (CC), and molecular functions (MF). For BP, the proteins were primarily involved in organelle localization and the regulation of oxidoreductase activity. For CC, enrichment was observed in pathways such as the zymogen granule membrane, COPI-coated vesicle membrane, zymogen granule, COPI-coated vesicle, Flemming body, spindle midzone, cleavage furrow, Golgi-associated vesicle membrane, ER to Golgi transport vesicle membrane, and cell division site. For MF, the proteins were enriched in functions including ferric iron binding, oxidoreductase activity, acting on metal ions, bioactive lipid receptor activity, protein disulfide isomerase activity, intramolecular oxidoreductase activity, transposing S-S bonds, ferrous iron binding, protein-disulfide reductase activity, gamma-tubulin binding, frizzled binding, and phosphatidylinositol-3,4,5-trisphosphate binding. The number of plasma proteins enriched in each pathway exceeded one, as indicated on the abscissa, and all enrichments were statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The highest confidence level was observed for biological processes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that the disease-associated plasma proteins were predominantly involved in ferroptosis, porphyrin metabolism, Vibro cholerae infection, sphingolipid metabolism, VEGF signaling pathway, thyroid hormone synthesis, Fc gamma R-mediated phagocytosis, Sphingolipid signaling pathway, and Apelin signaling pathway. Among these pathways, the highest statistical confidence (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) was observed for ferroptosis and porphyrin metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Furthermore, GeneMANIA network analysis demonstrated extensive functional interactions among the plasma protein phenotype genes, with co-expression accounting for 92.52% and predicted interactions representing 7.48% of the gene interaction network (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). These findings highlight the complex functional relationships and potential biological mechanisms underlying the roles of these plasma proteins in cervical cancer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSensitivity analysis of the forward Mendelian randomization (MR) study with cervical cancer (CC) as the outcome.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eHeterogeneity analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003ePleiotropy analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eInverse variance\u003c/p\u003e \u003cp\u003eweighted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ_df\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ_pval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ_df\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ_pval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEgger_intercept\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ese\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003epval\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDEFB135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.7523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.8838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.5969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.1151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.4201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.5316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.9013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.2344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.2618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.5776\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFTMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.5965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.5133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.7875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.0166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.7795\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePDIA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.9094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.5884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.2741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.3062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.6679\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRACGAP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.5805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.0124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.5309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.4684\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPHK2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.0807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.4094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.4942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.2346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.4987\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTMED2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.4461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.4457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.0052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.4426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.9926\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eLeveraging large-scale protein quantitative trait locus (pQTL) data and publicly available genome-wide association study (GWAS) summary statistics, we employed Mendelian randomization (MR) analysis to investigate the causal relationship between 4,907 plasma proteins and cervical cancer. Our analysis identified seven plasma proteins with significant causal associations: DEFB135, FGL2, FTMT, PDIA4, SPHK2, and TMED2, which may have protective effects against cervical cancer, whereas RACGAP1 may promote cervical carcinogenesis. We conducted sensitivity analyses to minimize the impact of heterogeneity and horizontal pleiotropy on our findings. Furthermore, reverse MR analysis showed no evidence that cervical carcinogenesis drives these plasma proteins' aberrant expression, reinforcing the robustness of the causal relationships. Functional enrichment analyses using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed that these plasma proteins primarily involve oxidoreductase activity and ferroptosis regulation pathways. Ferroptosis is characterized by iron-dependent lipid peroxidation, which disrupts cellular membranes and culminates in cell death. This process has been implicated in the pathogenesis of various diseases, including cancer. GeneMANIA analysis indicated that the seven plasma proteins identified as phenotypic genes were situated at the center of the network, forming a dense web of interactions with surrounding genes. These findings provide mechanistic insights into the molecular underpinnings of cervical cancer and highlight potential therapeutic targets, offering a foundation for further experimental and clinical investigations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe DEFB135 gene encodes defensin beta 135, an antimicrobial peptide critical in modulating immune responses and inflammation, predominantly expressed in human epithelial tissues[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Previous studies have shown that DEFB135 expression levels are associated with susceptibility to certain infectious diseases. Cervical cancer is primarily driven by persistent infection with high-risk human papillomavirus (HPV); it is possible that HPV-mediated downregulation of DEFB135 expression could impair the host's immune defense mechanisms. Although no direct link between DEFB135 and oncogenesis has been established, our Mendelian randomization (MR) analysis revealed a significant inverse association between DEFB135 and cervical cancer risk. This finding suggests that DEFB135 may be protective in mitigating disease progression and could represent a potential therapeutic target. However, the precise molecular mechanisms underlying this relationship remain to be elucidated and warrant further investigation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFibrinogen-like protein 2 (FGL2), a member of the fibrinogen superfamily, is characterized by its thromboplastin activity and diverse immunomodulatory functions. FGL2 is frequently overexpressed in various tumor tissues [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and has been implicated in the malignant progression of low-grade gliomas (LGGs) to high-grade gliomas (HGGs)[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Studies have also demonstrated that FGL2 promotes macrophage polarization and enhances the proliferation of regulatory T cells (Tregs) within the tumor microenvironment, thereby amplifying immunosuppressive activity[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In hepatocellular carcinoma, FGL2 overexpression has been closely linked to tumor growth and angiogenesis, suggesting a potential parallel role in cervical cancer[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. However, the functional significance of FGL2 in cervical cancer remains poorly understood. Our findings indicate that elevated FGL2 expression may have a protective effect against cervical cancer, highlighting the need for further \u003cem\u003eex vivo\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e studies to elucidate the mechanism of this association.\u003c/p\u003e \u003cp\u003eMitochondrial ferritin (FTMT) is a protein preferentially expressed in cells with high metabolic activity and oxygen-deprived environments, where it functions to sequester iron and mitigate oxidative damage. Fan et al. demonstrated that FTMT expression is inversely correlated with gastric cancer progression [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Wu et al. further revealed that FTMT expression is upregulated by hypoxia-inducible factor 1α (HIF-1α), enhancing cellular protection under hypoxic conditions[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Additionally, FTMT overexpression can modulate mitochondrial iron availability, potentially contributing to ineffective erythropoiesis, a process implicated in cancer progression[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Given that cervical cancer development is strongly associated with human papillomavirus (HPV) infection, which elevates intracellular oxidative stress, FTMT may play a protective role by reducing reactive oxygen species (ROS)-induced DNA damage and suppressing carcinogenesis. These insights align with our findings, suggesting that FTMT may protect against cervical cancer.\u003c/p\u003e \u003cp\u003eProtein disulfide isomerase A4 (PDIA4), an endoplasmic reticulum-resident protein, is critical in protein folding and disulfide bond formation. Previous studies have reported that PDIA4 is aberrantly overexpressed in cervical cancer tissues[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Kaplan-Meier survival analyses indicate that elevated PDIA4 expression is associated with poorer clinical outcomes in cervical cancer patients. Functional studies have demonstrated that PDIA4 knockdown significantly suppresses the proliferation and migration of cervical cancer cells. Additionally, evidence suggests that PDIA4 may promote tumor progression by modulating apoptosis and DNA repair pathways, with its overexpression correlating with reduced survival in cervical cancer patients[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. However, our Mendelian randomization (MR) analysis revealed an inverse association between PDIA4 expression and cervical cancer risk, contrasting with prior findings. This discrepancy underscores the need for further mechanistic studies to clarify the role of PDIA4 in cervical cancer pathogenesis.\u003c/p\u003e \u003cp\u003eSphingosine kinase 2 (SPHK2), one of the two isoforms of sphingosine kinase, plays a complex role in cancer biology. Previous studies have demonstrated that SPHK2 regulates intracellular sphingosine-1-phosphate (S1P) levels, influencing cellular growth, survival, and migration [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In cervical cancer, SPHK2 overexpression has been associated with enhanced tumor invasiveness and resistance to therapy. At the same time, its inhibition has been shown to suppress the proliferation and migration of cervical cancer cells [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Additionally, the pharmacological inhibition of SPHK2 has been reported to sensitize cervical cancer cells to chemotherapeutic agents, potentially overcoming treatment resistance. However, our findings suggest a protective role for SPHK2 in cervical cancer, contrasting with previous reports. This discrepancy highlights the need for further investigation into the precise mechanisms by which SPHK2 influences cervical cancer progression and therapy resistance.\u003c/p\u003e \u003cp\u003eTransmembrane p24 transport protein 2 (TMED2) is a key regulator of vesicular protein transport within the cytoplasm. Fang et al. demonstrated that TMED2 is significantly overexpressed in breast cancer tissues at both the mRNA and protein levels[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Similarly, elevated TMED2 expression has been observed in head and neck squamous cell carcinoma, a biomarker for poor prognosis[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The oncogenic role of TMED2 is further corroborated by studies in other malignancies, including hepatocellular carcinoma, ovarian cancer, lung cancer, and chordoma[\u003cspan additionalcitationids=\"CR42 CR43 CR44 CR45\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. However, the relationship between TMED2 and cervical cancer remains poorly characterized. Our findings suggest that TMED2 may function as a protective factor in cervical cancer, highlighting its potential as a novel therapeutic target for this disease.\u003c/p\u003e \u003cp\u003eRac GTPase-activating protein 1 (RACGAP1) is a specific RhoGAP that regulates Rac1 and Cdc42 signaling, thereby driving tumor progression[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Zhang et al. demonstrated that RACGAP1 modulates c-Jun expression via miR-192, and c-Jun, through p-JNK phosphorylation, activates AP-1, promoting the proliferation, migration, and invasion of cervical cancer cells[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Additionally, Ruan et al. showed that RACGAP1 Speeds up cell cycle progression by regulating CDC25C in cervical cancer cells[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Recent studies further revealed that FOXM1 synergizes with RACGAP1, inhibiting apoptosis mediated through the PI3K/AKT signaling pathway[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. These findings are consistent with our results, suggesting that RACGAP1 may act as a risk factor for cervical cancer and may represent a potential therapeutic target.\u003c/p\u003e \u003cp\u003eOur study has several strengths. First, we leveraged protein quantitative trait locus (pQTL) data from the most extensive proteomics genome-wide association study (GWAS) database, enabling a comprehensive and robust analysis. Second, the Mendelian randomization (MR) design effectively minimized confounding factors and reduced the risk of reverse causality, strengthening the validity of our causal inferences. Third, we applied false discovery rate (FDR) correction and prioritized plasma proteins with consistent odds ratio (OR) directions across analyses, further enhancing the reliability of our findings. However, several limitations should be acknowledged. First, although restricting our analysis to European populations reduced bias related to population stratification, the lack of data from other ethnic groups may limit the generalizability of our results and introduce potential biases. Second, the functional enrichment analyses were predictive, and the biological relevance of these findings requires further experimental validation. Addressing these limitations in future studies will be critical to extending the applicability and impact of our results. Despite these limitations, our study provides novel genetic insights into the molecular pathogenesis of cervical cancer and identifies promising therapeutic targets. These findings can potentially inform the development of more effective treatment strategies and ultimately improve patient clinical outcomes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur results indicate that DEFB135, FGL2, FTMT, PDIA4, SPHK2, and TMED2 may protect against cervical cancer, whereas RACGAP1 may promote disease pathogenesis. These findings provide a robust theoretical base for identifying potential biomarkers and therapeutic targets for cervical cancer, offering new avenues for further mechanistic and clinical investigations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eSupplementary material\u003c/h2\u003e \u003cp\u003eThe supplementary materials accompanying this article are provided in the associated documentation.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003eThe relevant local ethics committees approved the study protocol, and all participants provided written informed consent after receiving detailed information about the study. Additionally, this investigation was conducted by the STROBE-MR guidelines.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by the following projects: General Project of the Joint Special Project of Local Universities in Yunnan Province (202001BA070001-064), Dali University Doctoral Research Start-up Fund Project (KYBS2018012), Open Project of Yunnan Provincial Key Laboratory of Entomological Biopharmaceutical R\u0026amp;D (AG2024002), Clinical Medicine Discipline Team Building Project of the First Affiliated Hospital of Dali University (DFYYB2024026), and Open Project of Key Laboratory of Screening and Research of Resistant Plant Resources in West Yunnan, Yunnan Province (APKL2101). General Project of the Joint Special Project of Local Universities in Yunnan Province(202101BA070001-102).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor contributionsYan-Hong Zhao: Formal analysis, Investigation, Resources, Visualization, Writing \u0026ndash; original draft. Qing-Fen Ruan: data curation Jing -Hua Ning: visualization. Xin Zhang: software, visualization. Run Qu: Validation. Jing Zou: Project administration, writing-review \u0026amp;editing. Yi Liang: supervision.Cheng-Gui Zhang: Project administration. Yu-Zhe Zhang: conceptualization, Project administration, methodology, supervision, visualization.FundingThis study was supported by the following projects: General Project of the Joint Special Project of Local Universities in Yunnan Province (202001BA070001-064), Dali University Doctoral Research Start-up Fund Project (KYBS2018012), Open Project of Yunnan Provincial Key Laboratory of Entomological Biopharmaceutical R\u0026amp;D (AG2024002), Clinical Medicine Discipline Team Building Project of the First Affiliated Hospital of Dali University (DFYYB2024026), and Open Project of Key Laboratory of Screening and Research of Resistant Plant Resources in West Yunnan, Yunnan Province (APKL2101). General Project of the Joint Special Project of Local Universities in Yunnan Province（202101BA070001-102）.AcknowledgementThe views and opinions expressed in this article are solely those of the authors and do not necessarily reflect the official policies or positions of their affiliated institutions, the publisher, the editors, or the reviewers. The publisher makes no representations or warranties, either expressed or implied, regarding the accuracy or completeness of the content, including any products evaluated in the article or the claims made by their manufacturers.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I and Jemal A (2024) Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians 74:229\u0026ndash;263. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.3322/caac.21834\u003c/span\u003e\u003cspan address=\"doi: 10.3322/caac.21834\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen PA, Jhingran A, Oaknin A and Denny L (2019) Cervical cancer. The Lancet 393:169\u0026ndash;182. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi:10.1016/s0140-6736(18)32470-x\u003c/span\u003e\u003cspan address=\"doi:10.1016/s0140-6736(18)32470-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTao T, Yang X, Qin Q, Shi W, Wang Q, Yang Y and He J (2017) NHERF1 Enhances Cisplatin Sensitivity in Human Cervical Cancer Cells. International Journal of Molecular Sciences 18. doi: 10.3390/ijms18010005\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuehn BM (2021) WHO Launches Global Push to Eliminate Cervical Cancer. Jama 325. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1001/jama.2020.25668\u003c/span\u003e\u003cspan address=\"doi: 10.1001/jama.2020.25668\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDhindsa RS, Burren OS, Sun BB, Prins BP, Matelska D, Wheeler E, Mitchell J, Oerton E, Hristova VA, Smith KR, Carss K, Wasilewski S, Harper AR, Paul DS, Fabre MA, Runz H, Viollet C, Challis B, Platt A, \u0026Aring;gren R, Anderson-Dring L, Atanur S, Baker D, Barrett C, Belvisi M, Bohlooly-Y M, Buvall L, Camacho N, Cazares L, Cameron-Christie S, Chen M, Cohen S, Danielson RF, Das S, Davis A, Deevi SVV, Ding W, Dougherty B, Fairhurst-Hunter Z, Garg M, Georgi B, Rangel CG, Haefliger C, Hammar M, Hanna RN, Hansen PBL, Harrow J, Henry I, Hess S, Hollis B, Hu F, Jiang X, Kundu K, Lai Z, Lal M, Lassi G, Liang Y, Lopes M, Lythgow K, MacArthur S, Maisuria-Armer M, March R, Martins C, Megy K, Menzies R, Micha\u0026euml;lsson E, Middleton F, Mowrey B, Muthas D, Nag A, O'Dell S, Ohne Y, Olsson H, O'Neill A, Ostridge K, Pullman B, Rae W, Raies A, Reznichenko A, Ros XR, Ryaboshapkina M, Sanganee H, Sidders B, Snowden M, Stankovic S, Stevens H, Tachmazidou I, Taiy H, Tian L, Underwood C, Walentinsson A, Wang Q-D, Zehir A, Zou Z, Vitsios D, Ashley EA, Whelan CD, Pangalos MN, Wang Q and Petrovski S (2023) Rare variant associations with plasma protein levels in the UK Biobank. Nature 622:339\u0026ndash;347. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1038/s41586-023-06547-x\u003c/span\u003e\u003cspan address=\"doi: 10.1038/s41586-023-06547-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCharakorn C, Thadanipon K, Chaijindaratana S, Rattanasiri S, Numthavaj P and Thakkinstian A (2018) The association between serum squamous cell carcinoma antigen and recurrence and survival of patients with cervical squamous cell carcinoma: A systematic review and meta-analysis. Gynecologic Oncology 150:190\u0026ndash;200. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1016/j.ygyno.2018.03.056\u003c/span\u003e\u003cspan address=\"doi: 10.1016/j.ygyno.2018.03.056\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou Y, Tao L, Qiu J, Xu J, Yang X, Zhang Y, Tian X, Guan X, Cen X and Zhao Y (2024) Tumor biomarkers for diagnosis, prognosis and targeted therapy. Signal Transduction and Targeted Therapy 9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1038/s41392-024-01823-2\u003c/span\u003e\u003cspan address=\"doi: 10.1038/s41392-024-01823-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFranceschi S (2021) Genomic characterisation of cervical cancer and human papillomavirus: new opportunities for precision medicine. Lancet Oncol 22:419\u0026ndash;420. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1016/s1470-2045(21)00058-9\u003c/span\u003e\u003cspan address=\"doi: 10.1016/s1470-2045(21)00058-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi S and Gong M (2024) Drug targets for haemorrhoidal disease: proteome-wide Mendelian randomisation and colocalisation analyses. Gut 73:e31-e31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1136/gutjnl-2023-330967\u003c/span\u003e\u003cspan address=\"doi: 10.1136/gutjnl-2023-330967\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSkrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM, Swanson SA, VanderWeele TJ, Higgins JPT, Timpson NJ, Dimou N, Langenberg C, Golub RM, Loder EW, Gallo V, Tybjaerg-Hansen A, Davey Smith G, Egger M and Richards JB (2021) Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement. JAMA 326:1614\u0026ndash;1621. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1001/jama.2021.18236\u003c/span\u003e\u003cspan address=\"doi: 10.1001/jama.2021.18236\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu Y, Hou L, Shi X, Sun X, Liu X, Yu Y, Yuan Z, Li H and Xue F (2022) Impact of nonrandom selection mechanisms on the causal effect estimation for two-sample Mendelian randomization methods. PLoS Genet 18:e1010107. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1371/journal.pgen.1010107\u003c/span\u003e\u003cspan address=\"doi: 10.1371/journal.pgen.1010107\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin J, Zhou J and Xu Y (2023) Potential drug targets for multiple sclerosis identified through Mendelian randomization analysis. Brain 146:3364\u0026ndash;3372. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1093/brain/awad070\u003c/span\u003e\u003cspan address=\"doi: 10.1093/brain/awad070\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu D, Yang X, Wu W and Yang J (2024) Identification of Novel Protein Biomarkers and Drug Targets for Acne Vulgaris by Integrating Human Plasma Proteome with Genome-Wide Association Data. J Inflamm Res 17:4431\u0026ndash;4441. doi: 10.2147/JIR.S463450\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong N, Shi P, Cui K, Zeng L, Wang Z, Di W, Li J, Fan Y, Li Z, Zhang J, Su W and Wang H (2024) Potential drug targets for tumors identified through Mendelian randomization analysis. Scientific Reports 14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1038/s41598-024-62178-w\u003c/span\u003e\u003cspan address=\"doi: 10.1038/s41598-024-62178-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerkingstad E, Sulem P, Atlason BA, Sveinbjornsson G, Magnusson MI, Styrmisdottir EL, Gunnarsdottir K, Helgason A, Oddsson A, Halldorsson BV, Jensson BO, Zink F, Halldorsson GH, Masson G, Arnadottir GA, Katrinardottir H, Juliusson K, Magnusson MK, Magnusson OT, Fridriksdottir R, Saevarsdottir S, Gudjonsson SA, Stacey SN, Rognvaldsson S, Eiriksdottir T, Olafsdottir TA, Steinthorsdottir V, Tragante V, Ulfarsson MO, Stefansson H, Jonsdottir I, Holm H, Rafnar T, Melsted P, Saemundsdottir J, Norddahl GL, Lund SH, Gudbjartsson DF, Thorsteinsdottir U and Stefansson K (2021) Large-scale integration of the plasma proteome with genetics and disease. Nat Genet 53:1712\u0026ndash;1721. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1038/s41588-021-00978-w\u003c/span\u003e\u003cspan address=\"doi: 10.1038/s41588-021-00978-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowden J, Del Greco MF, Minelli C, Davey Smith G, Sheehan NA and Thompson JR (2016) Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic. Int J Epidemiol 45:1961\u0026ndash;1974. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1093/ije/dyw220\u003c/span\u003e\u003cspan address=\"doi: 10.1093/ije/dyw220\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeng Y, Tan Z, Su Y, Li L and Chen C (2023) Causal association between common rheumatic diseases and glaucoma: a Mendelian randomization study. Front Immunol 14:1227138. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.3389/fimmu.2023.1227138\u003c/span\u003e\u003cspan address=\"doi: 10.3389/fimmu.2023.1227138\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang L, Zhang C, Zhang J, Liu A, Wang P and Xu J (2023) A Bidirectional Mendelian Randomization Study of Sarcopenia-Related Traits and Knee Osteoarthritis. Clin Interv Aging 18:1577\u0026ndash;1586. doi: 10.2147/CIA.S424633\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePierce BL and Burgess S (2013) Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators. Am J Epidemiol 178:1177\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1093/aje/kwt084\u003c/span\u003e\u003cspan address=\"doi: 10.1093/aje/kwt084\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowden J, Davey Smith G, Haycock PC and Burgess S (2016) Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol 40:304\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1002/gepi.21965\u003c/span\u003e\u003cspan address=\"doi: 10.1002/gepi.21965\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowden J, Davey Smith G and Burgess S (2015) Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol 44:512\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1093/ije/dyv080\u003c/span\u003e\u003cspan address=\"doi: 10.1093/ije/dyv080\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen JF, Chalumeau M, Cohen R, Korevaar DA, Khoshnood B and Bossuyt PM (2015) Cochran's Q test was useful to assess heterogeneity in likelihood ratios in studies of diagnostic accuracy. J Clin Epidemiol 68:299\u0026ndash;306. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1016/j.jclinepi.2014.09.005\u003c/span\u003e\u003cspan address=\"doi: 10.1016/j.jclinepi.2014.09.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerbanck M, Chen CY, Neale B and Do R (2018) Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 50:693\u0026ndash;698. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1038/s41588-018-0099-7\u003c/span\u003e\u003cspan address=\"doi: 10.1038/s41588-018-0099-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHemani G, Tilling K and Davey Smith G (2017) Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet 13:e1007081. doi: 10.1371/journal.pgen.1007081\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBulgay C, Kasakolu A, Bıyıklı T, Koncagul S, Kazan HH, Ahmetov II, Ergun MA, Griffiths MD and Szabo A (2025) Genome-Wide Association Study of Exercise Addiction Among Elite Wrestlers. Brain Sciences 15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.3390/brainsci15020102\u003c/span\u003e\u003cspan address=\"doi: 10.3390/brainsci15020102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan J, Zhao Q, Gabrusiewicz K, Kong L-Y, Xia X, Wang J, Ott M, Xu J, Davis RE, Huo L, Rao G, Sun S-C, Watowich SS, Heimberger AB and Li S (2019) FGL2 promotes tumor progression in the CNS by suppressing CD103\u0026thinsp;+\u0026thinsp;dendritic cell differentiation. Nature Communications 10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1038/s41467-018-08271-x\u003c/span\u003e\u003cspan address=\"doi: 10.1038/s41467-018-08271-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShalev I, Liu H, Koscik C, Bartczak A, Javadi M, Wong KM, Maknojia A, He W, Liu MF, Diao J, Winter E, Manuel J, McCarthy D, Cattral M, Gommerman J, Clark DA, Phillips MJ, Gorczynski RR, Zhang L, Downey G, Grant D, Cybulsky MI and Levy G (2008) Targeted Deletion of fgl2 Leads to Impaired Regulatory T Cell Activity and Development of Autoimmune Glomerulonephritis. The Journal of Immunology 180:249\u0026ndash;260. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.4049/jimmunol.180.1.249\u003c/span\u003e\u003cspan address=\"doi: 10.4049/jimmunol.180.1.249\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMelnyk MC, Shalev I, Zhang J, Bartczak A, Gorczynski RM, Selzner N, Inman R, Marsden PA, Phillips MJ, Clark DA and Levy GA (2011) The prothrombinase activity of FGL2 contributes to the pathogenesis of experimental arthritis. Scandinavian Journal of Rheumatology 40:269\u0026ndash;278. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.3109/03009742.2010.536163\u003c/span\u003e\u003cspan address=\"doi: 10.3109/03009742.2010.536163\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan J, Zhao Q, Wang J, Tian X, Wang J, Xia X, Ott M, Rao G, Heimberger AB and Li S (2021) FGL2-wired macrophages secrete CXCL7 to regulate the stem-like functionality of glioma cells. Cancer Letters 506:83\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1016/j.canlet.2021.02.021\u003c/span\u003e\u003cspan address=\"doi: 10.1016/j.canlet.2021.02.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLatha K, Yan J, Yang Y, Gressot LV, Kong L-Y, Manyam G, Ezhilarasan R, Wang Q, Sulman EP, Eric Davis R, Huang S, Fuller GN, Rao A, Heimberger AB, Li S and Rao G (2019) The Role of Fibrinogen-Like Protein 2 on Immunosuppression and Malignant Progression in Glioma. JNCI: Journal of the National Cancer Institute 111:292\u0026ndash;300. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1093/jnci/djy107\u003c/span\u003e\u003cspan address=\"doi: 10.1093/jnci/djy107\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Xu L, Zeng Q, Wang J, Wang M, Xi D, Wang X, Yang D, Luo X and Ning Q (2012) Downregulation of FGL2/prothrombinase delays HCCLM6 xenograft tumour growth and decreases tumour angiogenesis. Liver International 32:1585\u0026ndash;1595. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1111/j.1478-3231.2012.02865.x\u003c/span\u003e\u003cspan address=\"doi: 10.1111/j.1478-3231.2012.02865.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan W, Wu Z, Xu S, Liu Z, Huang Y and Wang P (2025) Mendelian randomization analysis of plasma proteins reveals potential novel tumor markers for gastric cancer. Scientific Reports 15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1038/s41598-025-88118-w\u003c/span\u003e\u003cspan address=\"doi: 10.1038/s41598-025-88118-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu Q, Wu W-S, Su L, Zheng X, Wu W-Y, Santambrogio P, Gou Y-J, Hao Q, Wang P-N, Li Y-R, Zhao B-L, Nie G, Levi S and Chang Y-Z (2019) Mitochondrial Ferritin Is a Hypoxia-Inducible Factor 1α-Inducible Gene That Protects from Hypoxia-Induced Cell Death in Brain. Antioxidants \u0026amp; Redox Signaling 30:198\u0026ndash;212. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1089/ars.2017.7063\u003c/span\u003e\u003cspan address=\"doi: 10.1089/ars.2017.7063\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInvernizzi R, Travaglino E, Della Porta MG, Gall\u0026igrave; A, Malcovati L, Rosti V, Bergamaschi G, Erba BG, Bellistri F, Bastia R, Santambrogio P, Levi S and Cazzola M (2013) Effects of mitochondrial ferritin overexpression in normal and sideroblastic erythroid progenitors. British Journal of Haematology 161:726\u0026ndash;737. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1111/bjh.12316\u003c/span\u003e\u003cspan address=\"doi: 10.1111/bjh.12316\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXing F, Song Z and Cheng Z (2022) High expression of PDIA4 promotes malignant cell behavior and predicts reduced survival in cervical cancer. Oncology Reports 48. doi: 10.3892/or.2022.8399\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Li T, Zhang L, Shangguan F, Shi G, Wu X, Cui Y, Wang Xe, Wang X, Liu Y, Lu B, Wei T, Wang C-c and Wang L (2019) Targeting the functional interplay between endoplasmic reticulum oxidoreductin-1α and protein disulfide isomerase suppresses the progression of cervical cancer. EBioMedicine 41:408\u0026ndash;419. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1016/j.ebiom.2019.02.041\u003c/span\u003e\u003cspan address=\"doi: 10.1016/j.ebiom.2019.02.041\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang L, Urtz N, Gaertner F, Legate KR, Petzold T, Lorenz M, Mazharian A, Watson SP and Massberg S (2013) Sphingosine kinase 2 (Sphk2) regulates platelet biogenesis by providing intracellular sphingosine 1-phosphate (S1P). Blood 122:791\u0026ndash;802. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1182/blood-2012-12-473884\u003c/span\u003e\u003cspan address=\"doi: 10.1182/blood-2012-12-473884\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Cheng L, Shi X, Song Y, Chen X-y, Chen M-b, Yao J, Zhang Z-q and Cai S (2022) The sphingosine kinase inhibitor SKI-V suppresses cervical cancer cell growth. International Journal of Biological Sciences 18:2994\u0026ndash;3005. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.7150/ijbs.71381\u003c/span\u003e\u003cspan address=\"doi: 10.7150/ijbs.71381\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFang Z, Song Y-X, Wo G-Q, Zhou H-L, Li L, Yang S-Y, Chen X, Zhang J and Tang J-H (2022) Screening of the novel immune-suppressive biomarkers of TMED family and whether knockdown of TMED2/3/4/9 inhibits cell migration and invasion in breast cancer. Annals of Translational Medicine 10:1280\u0026ndash;1280. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.21037/atm-22-5444\u003c/span\u003e\u003cspan address=\"doi: 10.21037/atm-22-5444\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao W, Zhang Z-W, Wang H-Y, Li X-D, Peng W-T, Guan H-Y, Liao Y-X and Liu A (2022) TMED2/9/10 Serve as Biomarkers for Poor Prognosis in Head and Neck Squamous Carcinoma. Frontiers in Genetics 13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.3389/fgene.2022.895281\u003c/span\u003e\u003cspan address=\"doi: 10.3389/fgene.2022.895281\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng H, Yang Y, Han J, Jiang W-h, Chen C, Wang M-c, Gao R, Li S, Tian T, Wang J, Ma L-j, Ren H and Zhou W-p (2016) TMED3 promotes hepatocellular carcinoma progression via IL-11/STAT3 signaling. Scientific Reports 6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1038/srep37070\u003c/span\u003e\u003cspan address=\"doi: 10.1038/srep37070\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie A, Xu X, Kuang P, Zhang L and Yu F (2021) TMED3 promotes the progression and development of lung squamous cell carcinoma by regulating EZR. Cell Death \u0026amp; Disease 12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1038/s41419-021-04086-9\u003c/span\u003e\u003cspan address=\"doi: 10.1038/s41419-021-04086-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Y-C, Chien M-H, Lai T-C, Tung M-C, Jan Y-H, Chang W-M, Jung S-M, Chen M-H, Yeh C-N and Hsiao M (2021) Proteomics-based identification of TMED9 is linked to vascular invasion and poor prognoses in patients with hepatocellular carcinoma. Journal of Biomedical Science 28. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1186/s12929-021-00727-5\u003c/span\u003e\u003cspan address=\"doi: 10.1186/s12929-021-00727-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, Hao HH, Zhuang HW, Wang J, Sheng Y, Xu F, Dou J, Chen C and Shen Y (2020) Circular RNA circ_0008305 aggravates hepatocellular carcinoma growth through binding to miR-186 and inducing TMED2. Journal of Cellular and Molecular Medicine 26:1742\u0026ndash;1753. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1111/jcmm.15945\u003c/span\u003e\u003cspan address=\"doi: 10.1111/jcmm.15945\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi-Peng G, Chun-Lin C, Huan W, Fan-Liang M, Yong-Ning C, Ya-Di Z, Guang-Ping Z and Ye-Ping C (2017) TMED2 promotes epithelial ovarian cancer growth. Oncotarget 8:94151\u0026ndash;94165. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.18632/oncotarget.21593\u003c/span\u003e\u003cspan address=\"doi: 10.18632/oncotarget.21593\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang J, Huang H, Xiao D, Duan Y, Zheng Y and Chen Z (2021) Knockdown of TMED3 inhibits cell viability and migration and increases apoptosis in human chordoma cells. International Journal of Oncology 58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.3892/ijo.2021.5195\u003c/span\u003e\u003cspan address=\"doi: 10.3892/ijo.2021.5195\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTour\u0026eacute; A, Dorseuil O, Morin L, Timmons P, J\u0026eacute;gou B, Reibel L and Gacon G (1998) MgcRacGAP, A New Human GTPase-activating Protein for Rac and Cdc42 Similar to Drosophila rotundRacGAP Gene Product, Is Expressed in Male Germ Cells. Journal of Biological Chemistry 273:6019\u0026ndash;6023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1074/jbc.273.11.6019\u003c/span\u003e\u003cspan address=\"doi: 10.1074/jbc.273.11.6019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang T, Wang C, Wang K, Liang Y, Liu T, Feng L and Yang X (2022) RacGAP1 promotes the malignant progression of cervical cancer by regulating AP-1 via miR-192 and p-JNK. Cell Death \u0026amp; Disease 13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1038/s41419-022-05036-9\u003c/span\u003e\u003cspan address=\"doi: 10.1038/s41419-022-05036-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuan X and Jiang J (2022) RACGAP1 promotes proliferation and cell cycle progression by regulating CDC25C in cervical cancer cells. Tissue and Cell 76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1016/j.tice.2022.101804\u003c/span\u003e\u003cspan address=\"doi: 10.1016/j.tice.2022.101804\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYao H, Li J, Zhou D, Pan X, Chu Y and Yin J (2024) FOXM1 transcriptional regulation of RacGAP1 activates the PI3K/AKT signaling pathway to promote the proliferation, migration, and invasion of cervical cancer cells. International Journal of Clinical Oncology 29:333\u0026ndash;344. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi: 10.1007/s10147-023-02452-5\u003c/span\u003e\u003cspan address=\"doi: 10.1007/s10147-023-02452-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-the-egyptian-national-cancer-institute","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jeci","sideBox":"Learn more about [Journal of the Egyptian National Cancer Institute](http://jenci.springeropen.com)","snPcode":"43046","submissionUrl":"https://submission.springernature.com/new-submission/43046/3","title":"Journal of the Egyptian National Cancer Institute","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Plasma proteins, cervical cancer, Mendelian randomization, and tumor markers","lastPublishedDoi":"10.21203/rs.3.rs-6551278/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6551278/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCervical cancer continues to pose a considerable challenge to global health, necessitating innovative approaches for improved diagnostics and personalized treatment strategies. Prior investigations have suggested that plasma proteins may play a role in the pathogenesis of cervical cancer; however, these studies do not confirm a causal relationship. To address this gap, conducted a large-scale Mendelian randomization (MR) study of the plasma proteome.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a two-sample bidirectional Mendelian randomization (MR) analysis of 4,907 plasma proteins using publicly available genome-wide association study (GWAS) summary statistics to investigate the causal relationship between plasma proteome and cervical cancer risk. Analytical methods included inverse variance weighting (IVW), weighted median, MR-Egger regression, and simple and weighted models. Additionally, we performed sensitivity analyses to evaluate heterogeneity and horizontal pleiotropy through Cochran's Q test, MR-Egger intercept, MR-PRESSO test, and leave-one-out analysis. We also applied false discovery rate (FDR) correction to the results of all inverse variance weighting (IVW) methods to identify the plasma proteins most strongly associated with cervical cancer. Finally, we enriched the most relevant plasma protein genes using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses and GeneMANIA to identify disease-related pathways.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAccording to the IVW method, seven plasma proteins are significantly associated with cervical cancer risk (FDR-adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Specifically, six proteins demonstrated protective factors: DEFB135 (OR\u0026thinsp;=\u0026thinsp;0.201, 95% CI\u0026thinsp;=\u0026thinsp;0.082\u0026ndash;0.492, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), FGL2 (OR\u0026thinsp;=\u0026thinsp;0.104, 95% CI\u0026thinsp;=\u0026thinsp;0.032\u0026ndash;0.338, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), FTMT (OR\u0026thinsp;=\u0026thinsp;0.612, 95% CI\u0026thinsp;=\u0026thinsp;0.465\u0026ndash;0.804, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), PDIA4 (OR\u0026thinsp;=\u0026thinsp;0.088, 95% CI\u0026thinsp;=\u0026thinsp;0.026\u0026ndash;0.295, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), SPHK2 (OR\u0026thinsp;=\u0026thinsp;0.102, 95% CI\u0026thinsp;=\u0026thinsp;0.030\u0026ndash;0.350, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and TMED2 (OR\u0026thinsp;=\u0026thinsp;0.045, 95% CI\u0026thinsp;=\u0026thinsp;0.008\u0026ndash;0.246, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, RACGAP1 (OR\u0026thinsp;=\u0026thinsp;1.755, 95% CI\u0026thinsp;=\u0026thinsp;1.286\u0026ndash;2.395, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was identified as a risk factor. Reverse MR analysis revealed no significant evidence of reverse causation (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) between cervical cancer and these plasma proteins. Functional enrichment analysis identified several biologically relevant pathways potentially involved in cervical cancer pathogenesis, including the establishment of organelle localization, regulation of oxidoreductase activity, Ferroptosis, and Porphyrin metabolism.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThese findings suggest that DEFB135, FGL2, FTMT, PDIA4, SPHK2, and TMED2 may protect against cervical cancer, while RACGAP1 may represent a potential risk factor. The identified tumor markers provide mechanistic insights into the molecular basis of cervical cancer and warrant further investigation in functional studies.\u003c/p\u003e","manuscriptTitle":"Bidirectional Mendelian Randomization identifies plasma proteins associated with Cervical Cancer risk","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-13 15:43:12","doi":"10.21203/rs.3.rs-6551278/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-25T00:01:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-24T12:43:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"94158454483838667775316220505691145718","date":"2025-09-24T12:32:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96547534097252962073418256131654422782","date":"2025-05-08T04:34:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-07T19:45:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-07T01:39:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-07T01:35:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of the Egyptian National Cancer Institute","date":"2025-04-29T00:52:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-the-egyptian-national-cancer-institute","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jeci","sideBox":"Learn more about [Journal of the Egyptian National Cancer Institute](http://jenci.springeropen.com)","snPcode":"43046","submissionUrl":"https://submission.springernature.com/new-submission/43046/3","title":"Journal of the Egyptian National Cancer Institute","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"eb762cbf-2521-492c-8e3c-42018a53d2c0","owner":[],"postedDate":"May 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-18T20:53:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-13 15:43:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6551278","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6551278","identity":"rs-6551278","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}